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Multi-document summarization (MDS) is a topic of much attention in extensive knowledge areas. Extractive MDS techniques intend to shrink the text from a document compilation by enclosing essential content and minimizing unnecessary data. MDS is more challenging than single document summarization and has several weaknesses, including an inaccurate selection of important sentences, a percentage of low coverage, and redundancy among the sentences. To address these issues, our proposed system focuses on pioneering an innovative automated extractive MDS approach. The process begins with original document pre-processing, followed by the extraction of features such as modified TF-IDF, Bag of Word (BOW), and concept similarity (CS) features. These features are then inputted into a Long Short-Term Memory (LSTM) framework. The model's weights are fine-tuned using the Improved Dingo Optimization (IDO) technique. The proposed model is evaluated on the Amazon Review and DUC-2002 datasets and compared its performance with various existing algorithms. The results demonstrated significant enhancements over baseline models, with an accuracy of 0.922862 for the Amazon Review dataset and 0.899730 for the DUC2002 dataset. These findings underscore the effectiveness of our developed technique in improving the accuracy of extractive multi-document summarization. [ABSTRACT FROM AUTHOR]

Nowadays, due to the constantly growing amount of textual information, automatic text summarization constitutes an important research area in natural language processing. In this work, we present a novel framework that combines semantic graph representations along with deep learning predictions to generate abstractive summaries of single documents, in an effort to utilize a semantic representation of the unstructured textual content in a machine-readable, structured, and concise manner. The overall framework is based on a well defined methodology for performing semantic graph parsing, graph construction, graph transformations for machine learning models and deep learning predictions. The employed semantic graph representation focuses on using the model of abstract meaning representation. Several combinations of graph construction and graph transformation methods are investigated to specify the most efficient of them for the machine learning models. Additionally, a range of deep learning architectures is examined, including a sequence-to-sequence attentive network, reinforcement learning, transformer-based architectures, and pre-trained neural language models. In this direction, a semantic graph representation of an original text is extracted, and then the present framework formulates the problem as a graph-to-summary learning problem to predict a summary of an original text. To the best of our knowledge, this formulation of graph-to-summary predictions in abstractive text summarization, without other intermediate steps in the machine learning phase, has not been presented in the relevant literature. Another important contribution is an introduction of a measure for assessing the factual consistency of the generated summaries in an effort to provide a qualitative evaluation. To assess the framework, an extensive experimental procedure is presented that uses popular datasets to evaluate key aspects of the proposed approach. The obtained results exhibit promising performance, validating the robustness of the proposed framework. [ABSTRACT FROM AUTHOR]

Automatic text summarization is an important challenge in natural language understanding. Automatic text summarization mainly includes extractive text summarization and abstractive text summarization. Extractive text summarization selects salient content from a document to form a summary, whereas abstractive summaries are formed by generating words and sentences. In this paper, we propose a novel abstractive summarization method incorporating graph knowledge. First, we propose a document word representation model based on a graph convolutional neural network for generating a summary. Then, the graph knowledge is integrated into an abstractive summarization model, which thus gains a better ability to generate new words. Finally, the abstractive summarization model is combined with a pointer generation model to solve the out-of-vocabulary problem. We apply our model to the Xsum and Gigaword summarization datasets, and the experimental results demonstrate that our model achieves state-of-the-art results on the Xsum dataset and results comparable to those of existing methods on the Gigaword dataset. [ABSTRACT FROM AUTHOR]

The number of scientific publications nowadays is rapidly increasing, causing information overload for researchers and making it hard for scholars to keep up to date with current trends and lines of work. Recent work has tried to address this problem by developing methods for automated summarization in the scholarly domain, but concentrated so far only on monolingual settings, primarily English. In this paper, we consequently explore how state-of-the-art neural abstract summarization models based on a multilingual encoder–decoder architecture can be used to enable cross-lingual extreme summaries of scholarly texts. To this end, we compile a new abstractive cross-lingual summarization dataset for the scholarly domain in four different languages, which enables us to train and evaluate models that process English papers and generate summaries in German, Italian, Chinese and Japanese. We present our new X-SCITLDR dataset for multilingual summarization and thoroughly benchmark different models based on a state-of-the-art multilingual pre-trained model, including a two-stage pipeline approach that independently summarizes and translates, as well as a direct cross-lingual model. We additionally explore the benefits of intermediate-stage training using English monolingual summarization and machine translation as intermediate tasks and analyze performance in zero- and few-shot scenarios. Finally, we investigate how to make our approach more efficient on the basis of knowledge distillation methods, which make it possible to shrink the size of our models, so as to reduce the computational complexity of the summarization inference. [ABSTRACT FROM AUTHOR]

Knowledge Graphs (KGs) have already proven their strength as a source of high-quality information for different tasks such as data integration, search, text summarization, and personalization. Another prominent research field that has been benefiting from the adoption of KGs is that of Recommender Systems (RSs). Feeding a RS with data coming from a KG improves recommendation accuracy, diversity, and novelty, and paves the way to the creation of interpretable models that can be used for explanations. This possibility of combining a KG with a RS raises the question whether such an addition can be performed in a plug-and-play fashion – also with respect to the recommendation domain – or whether each combination needs a careful evaluation. To investigate such a question, we consider all possible combinations of (i) three recommendation tasks (books, music, movies); (ii) three recommendation models fed with data from a KG (and in particular, a semantics-aware deep learning model, that we discuss in detail), compared with three baseline models without KG addition; (iii) two main encyclopedic KGs freely available on the Web: DBpedia and Wikidata. Supported by an extensive experimental evaluation, we show the final results in terms of accuracy and diversity of the various combinations, highlighting that the injection of knowledge does not always pay off. Moreover, we show how the choice of the KG, and the form of data in it, affect the results, depending on the recommendation domain and the learning model. [ABSTRACT FROM AUTHOR]

Automatic text summarization is a lucrative field in natural language processing (NLP). The amount of data flow has multiplied with the switch to digital. The massive datasets hold a wealth of knowledge and information must be extracted to be useful. This article focusses on creating an unmanned text summarizing structure that accepts text as data feeded into the system to outputs a summary using a cutting-edge machine learning model. Advancements in NLP led to the introduction of transformers in the field and their outstanding performance pulled a lot of attention towards them. The two transformer-based language models namely, Bidirectional and Auto-regressive Transformer (BART) and Text-To-Text Transfer Transformer (T5) were implemented on the CNN_dailymail dataset. BART outperforms T5 by 3.02% in ROUGE-1 Score. The model provides a worthier performance in comparison to the other models introduced in the existing literature for performing the same task. [ABSTRACT FROM AUTHOR]

Multi-Document Summarization is the progression of extracting the pertinent information from a group of documents and weeding out the irrelevant information to create a concise text representation of the documents. This summarization initiates comprehensive information and a coherent flow of the summary, which reduces the effort and time of the individuals. In this research, the document summarization is performed using the Elytron Vespid-B rank-based Bidirectional Long Short-Term Memory (BiLSTM) classifier, which reduces the multi documents into a summarized document. The main contribution of the research depends on the proposed Elytron Vespid Beetle (Elytron Vespid-B) optimization algorithm that effectively enhanced the interactive and individual behavior of the Elytron-B that reduced the computational complexity of the classifier by optimally tuning the hyperparameters. The proposed Elytron vespid-B rank BiLSTM classifier achieved the values of 0.61, 0.35, and 0.61, for the metrics f1 measure for the varied rouges 1, 2, and l, which are believed to be more efficient than the existing approaches, and used to reveal the effectiveness of the research. [ABSTRACT FROM AUTHOR]

A long-term objective of artificial intelligence is to design an abstractive text summarization (ATS) system that can produce condensed, adequate, and realistic summaries for the source documents. Deep learning approaches have contributed significantly to recent advancements in ATS, taking the state-of-the-art to new heights. Despite the considerable success of earlier approaches, producing high-quality and more human-like abstractive summaries continues to be a challenging task in reality. In this paper, we present an adversarial framework for abstractive text summarization, in which a generative model G and a proposed deep discriminative model D are trained in an adversarial manner. The goal of the generator is to generate summaries that are hard to differentiate from real summaries, whereas the discriminator's role is to estimate the probability that a summary came from the training data rather than the generator to direct the generative model's training. Experimental results on a benchmark CNN/Daily Mail dataset demonstrate that the proposed model achieves Rouge-1 and Rouge-L scores of 41.58 and 38.96 respectively which are better than the ones reported by various other methods (e.g. the base paper [1] achieved Rouge-1 and Rouge-L score of 39.92 and 36.71, while one of the recent works ACGT [2] achieved Rouge-1 score of 40.49 and Rouge-L score of 37.41). The manuscript's unique characteristic is qualitative evaluation, which, along with quantitative evaluation, shows that the proposed model is superior. [ABSTRACT FROM AUTHOR]

While the world is battling COVID-19 pandemic and its variants; netizens are combating Infodemic – "Proliferation of fake news online". Spread of fake news during this global pandemic COVID-19 has dangerous consequences. Precise automated fake news detection is the need of the hour. This is the driving force behind this study. Intrinsic quality of news data: precision and objectivity can be studied to detect the credibility. However, to gain the knowledge or inference from it further it is a challenge. To address this challenge, this work proposes derivation of features into two categories: intrinsic word level features (Fine-grained) and sentence level features (Coarse- grained).For experimentation, fine-grained features are learned from word vector representation of the news articles. Psycho-linguistic and sentiment level word level features are derived using Empath library. Coarse-grained features (sentence-level) comprise of vectors generated by Text summarization and DOC2Vec. Taking the advantages of existing approaches, this paper proposes a new framework Granularity based Fake news Detection (GRAFED) that explores fusion of fine and coarse-grained features. The fusion of feature set is more powerful than individual fine or coarse-grained feature sets as they can capture complex interdependence of words in the sentence along with the semantics. Exhaustive experimentation using traditional classifiers with hybrid granular feature vector of GRAFED outperformed the existing approaches for publicly available state-of-art LIAR dataset. Experimental results show that the hybrid feature set is superior to individual feature set and the results are promising when compared to the existing state-of-art approaches as analyzed in the comparative analysis section. [ABSTRACT FROM AUTHOR]

Abstractive summarization is one of the popular topics that has been the researchers' attention for several years. This is because of the widespread application frameworks included in this field. Most of the existing summarization frameworks cannot provide effective abstracts as the contextual information of the input is not given importance. To deal with the problem, this work introduces a hierarchical framework using transformer technology to produce effective abstracts. The proposed framework includes preprocessing, extractive summarization, and abstractive summarization as the basic steps of the work. Initially, the input contents are preprocessed to obtain a clean document, and then the contents are provided to the extractive summarization unit. This unit consists of a fine-tuned BERTSum model (FTBS), which is a pre-trained model to produce the required extractive summary. The output is then provided to the proposed convolutional bidirectional gated recurrent unit transformer (CBi-GRUT) model, where an additional encoder model is introduced with the traditional transformer technology to obtain the output. The outcomes of the model are then assessed with the existing models to prove its efficacy, and the evaluations are carried out using the CNN/Daily Mail dataset. The proposed method achieved an average ROUGE-1 score of 0.78, average ROUGE-2 score of 0.68 and an average ROUGE-L score of 0.77. [ABSTRACT FROM AUTHOR]

This research addresses the limitations of pretrained models (PTMs) in generating accurate and comprehensive abstractive summaries for scientific articles, with a specific focus on the challenges posed by medical research. The proposed solution named medical text simplification and summarization (MedTSS) introduces a dedicated module designed to enrich source text for PTMs. MedTSS addresses issues related to token limits, reinforces multiple concepts, and mitigates entity hallucination problems without necessitating additional training. Furthermore, the module conducts linguistic analysis to simplify generated summaries, particularly tailored for the complex nature of medical research articles. The results demonstrate a significant enhancement, with MedTSS improving the Rouge-1 score from 16.46 to 35.17 without requiring additional training. By emphasizing knowledge-driven components, this framework offers a distinct perspective, challenging the common narrative of 'more data' or 'more parameters.' This alternative approach, especially applicable in health-related domains, signifies a broader contribution to the field of NLP. MedTSS serves as an innovative model that not only addresses the intricacies of medical research summarization but also presents a paradigm shift with implications for diverse domains beyond its initial scope. [ABSTRACT FROM AUTHOR]

Purpose: The conclusion section of a radiology report is crucial for summarizing the primary radiological findings in natural language and essential for communicating results to clinicians. However, creating these summaries is time-consuming, repetitive, and prone to variability and errors among different radiologists. To address these issues, we evaluated a fine-tuned Text-To-Text Transfer Transformer (T5) model for abstractive summarization to automatically generate conclusions for neuroradiology MRI reports in a low-resource language. Methods: We retrospectively applied our method to a dataset of 232,425 neuroradiology MRI reports in Spanish. We compared various pre-trained T5 models, including multilingual T5 and those newly adapted for Spanish. For precise evaluation, we employed BLEU, METEOR, ROUGE-L, CIDEr, and cosine similarity metrics alongside expert radiologist assessments. Results: The findings are promising, with the models specifically fine-tuned for neuroradiology MRI achieving scores of 0.46, 0.28, 0.52, 2.45, and 0.87 in the BLEU-1, METEOR, ROUGE-L, CIDEr, and cosine similarity metrics, respectively. In the radiological experts' evaluation, they found that in 75% of the cases evaluated, the conclusions generated by the system were as good as or even better than the manually generated conclusions. Conclusion: The methods demonstrate the potential and effectiveness of customizing state-of-the-art pre-trained models for neuroradiology, yielding automatic MRI report conclusions that nearly match expert quality. Furthermore, these results underscore the importance of designing and pre-training a dedicated language model for radiology report summarization. [ABSTRACT FROM AUTHOR]

Two of the most crucial issues for text summarization to generate faithful summaries are to make use of knowledge beyond text and to make use of cross-sentence relations in text. Intuitive ways for the two issues are knowledge graph (KG) and graph neural network (GNN), respectively. Entities are semantic units in text and in KG. This paper focuses on both issues by leveraging entities mentioned in text to connect GNN and KG for summarization. Firstly, entities are leveraged to construct a sentence-entity graph with weighted multi-type edges to model sentence relations, and a relational heterogeneous GNN for summarization is proposed to calculate node encodings. Secondly, entities are leveraged to link the graph to KG to collect knowledge. Thirdly, entities guide a two-step summarization framework defining a multitask selector to select salient sentences and entities, and using an entity-focused abstractor to compress the sentences. GNN is connected with KG by constructing sentence-entity graphs where entity–entity edges are built based on KG, initializing entity embeddings on KG, and training entity embeddings using entity–entity edges. The relational heterogeneous GNN utilizes both edge weights and edge types in GNN to calculate graphs with weighted multi-type edges. Experiments show the proposed method outperforms extractive baselines including the HGNN-based HGNNSum and abstractive baselines including the entity-driven SENECA on CNN/DM, and outperforms most baselines on NYT50. Experiments on sub-datasets show the density of sentence-entity edges greatly influences the performance of the proposed method. The greater the density, the better the performance. Ablations show effectiveness of the method. [ABSTRACT FROM AUTHOR]

The biomedical field specialists often face difficulties in summarization of available online literature. The biomedical archives like PubMed contain large set of lengthy documents, which needs to be shortened for further analysis of relevant data. In this work, we have introduced Similarity-specific Teaching and Learning-based optimization (Sim-TLBO) framework, which is combined with controlled stochastic selection of most relevant sentences for effective summarization. This method of utilizing metaheuristic teaching–learning approach along with multiple objective functions analysis provided an agile and robust solution. The output summary from the proposed solution is evaluated and compared based on ROUGE performance and various objective parameters including cohesion, non-redundancy, coverage, and readability. The hybrid text similarity (HTS) based on BERT similarity technique resulted in optimum ROUGE-1 values of 0.82 and 0.79 at 50% and 30% compression rates, respectively. The results from the presented framework when compared to other benchmark and baseline summarization models demonstrated significant performance improvement. [ABSTRACT FROM AUTHOR]

This paper presents a query-based extractive text summarization approach by using sense-oriented semantic relatedness measure. To find the query relevant sentences, we have to find semantic relatedness measure between query and input text sentences. To find the relatedness score, we need to know the exact sense of the words present in query and input text sentences. Word sense disambiguation (WSD) finds the actual meaning of a word according to its context of the sentence. We have proposed a WSD technique to extract query relevant sentences which is used to find a sense-oriented sentence semantic relatedness score between the query and input text sentence. Here, a feature-based method is presented to find semantic relatedness score between query and input text sentence. Finally the proposed query-based text summary method uses relevant and redundancy-free features to form cluster. There is a high probability that same featured cluster may contain redundant sentences. Therefore, a redundancy removal method is proposed to get redundancy-free sentences. In the end, redundancy-free query relevant sentences are obtained with an information rich summary. We have evaluated our proposed WSD technique with other existing methods by using Senseval and SemEval datasets and proposed Sense-Oriented Sentence Semantic Relatedness Score by using Li et al. dataset. We compare our proposed query-based extractive text summarization method with other methods participated in Document Understanding Conference and as well as with current methods. Evaluation and comparison state that the proposed query-based extractive text summarization method outperforms many existing and recent methods. [ABSTRACT FROM AUTHOR]

Pretrained models in abstractive summarization enable the summarization model to generate coherent summaries flexibly, but it also introduces unfactual hallucinations when generating novel words. To circumvent this drawback, we propose A Guided Tensor Product framework for abstractive Summarization (GTPSum). GTPSum utilizes highlighted sentence guidance signal and role embedding in the form of guided tensor product representations (GTPRs) to enhance the generation capacity for short document text. The guidance signal assists the model in focusing on crucial information, while the role embedding aids GTPSum in capturing the syntactic structure relationships present in the sentences. By employing this approach, the framework can not only focus on the essential information from the source document but also grasp the inherent sentence structure, resulting in generated summaries that closely align with the original content. The experimental results demonstrate the strong performance of our approach on the CNN/Daily Mail (44.28/20.88/41.08 ROUGE-1/-2/-L) and performed well on the other three datasets. Moreover, the comparison between our model generated summaries and those generated by the baseline models through human evaluation shows that our summaries contain more critical information, have better readability, and still maintain succinctness. [ABSTRACT FROM AUTHOR]

Long documents such as scientific papers and government reports, often discuss substantial issues in long conversation, which are time-consuming to read and understand. Generating abstractive summaries can help readers quickly grasp the main topics, yet prior work has mostly focused on short texts and also has some of the drawbacks such as out-of-vocabulary (OOV), mismatched sentences and meaning less summary. Hence, to overcome these issues the hybrid approach for automatic text summarization using the TextR-BLG pointer algorithm. In this designed model, the long document is given as an input for automatic text summarization and are evaluated for the word frequency length, based on the threshold value the sentences are split into extractive and abstractive approach. The text algorithm used in this model finds out the sentence similarity score and is validated with the plotted graph. Likewise, the sentences above the threshold level are considered as the abstractive approach. The optimized BERT-LSTM-BiGRU (BLG) based pointer algorithm is used for learning the meaning from the sentences by word embedding, encoding and decoding the hidden states. Finally, the reframed sentences are considered for attaining the similarity score and plotting graph. The sentences from the abstractive and extractive approach are ranked based on the plotted graph for generating the summary. For evaluating the performance of the model, the ROUGE 1, ROUGE 2, ROUGE L, Bert Score, Bleu Score, and Meteor Score are 59.2, 58.4, 62.3, 0.92, 0.78, and 0.67, which are compared with the existing model. From the evaluation of proposed and existing summarization techniques, the proposed model attains better text summarization than the existing model. Thus, the hybrid approach for automatic text summarization using the TextR-BLG pointer algorithm performs better by handling out-of-vocabulary words than the existing text summarization techniques. [ABSTRACT FROM AUTHOR]

Day by day, the amount of online and offline text data is growing tremendously from various sources like legal documents, medical documents, news articles, etc. Manual text summarization of large documents is unfeasible and costly because it takes much time and requires more effort. As a consequence, various graph-based text summarization techniques have been designed which provide thoroughly and well-prepared summaries of documents. The problems issues that exist in these techniques are redundancy of data, loss of information and readability. To overcome these problems, we have proposed a textual graph-based extractive text summarization technique called TGETS, for extracting essential information from a single document. In the proposed approach, a graph's node is denoted as group of sentences in the document and an edge of the graph is represented as an association between two sentences. The summary generation is based on the sum of sentence weight and the average weight of the textual graph. The performance of proposed approach is evaluated on the BBC news articles dataset through the ROUGE-metric ( R 1 and R 2 ). The proposed approach in the range of 100-200 words length summary offers better scores of 19.88%, 38.76%, and 30.73% for R 1 under precision, recall and F 1 -score with respect to the existing PageRank (PR) method. Similarly, for R 2 , the proposed approach exceeds by 32%, 26.99%, and 29.01% for precision, recall, and F 1 -score with respect to existing PageRank (PR) method. [ABSTRACT FROM AUTHOR]

The purpose of government news summarization is to extract the most important information from official government news reports. It is important for readers to be able to understand government news quickly in the age of information overload. Compared with other types of news, government news reports have more detailed content and possess a more normative format, resulting in greater length. To resolve the contradiction between the length of these reports and the trend of fragmented reading, this research proposes an automatic text summarization model based on multiple features. First, the features are extracted using the TF–IDF algorithm and word vector embedding method based on the bidirectional encoder representation from the transformers model. Second, we score the sentences based on the position, keywords, and similarity features. Finally, the top-ranked sentence is selected to form the summarization. To verify the effectiveness and superiority of the proposed method, Edmundson and ROUGE were adopted. First, based on the Edmundson evaluation criteria, the summarization results of various methods were scored. The score differences between ATS summarization based on the proposed method and manual summarization were minimal across consistency, grammaticality, time sequence, conciseness, and readability, with values of 0.14, 0.18, 0.12, 0.10, and 0.16, respectively. This suggests that our summarization exhibits the highest similarity to manual summarization. Second, we evaluated the summarization results using the ROUGE evaluation criteria. The results indicate that the proposed method achieved significantly higher scores compared to other models. Specifically, for character-level ROUGE-1, the P, R, and F scores reached 0.84, 0.93, and 0.88, respectively. At the word-level ROUGE-1, the P, R, and F scores were 0.81, 0.89, and 0.85, respectively, which demonstrates a noticeable improvement over other models. Furthermore, compared to manual methods, the proposed method has advantages in assisting the reader to obtain important information rapidly. [ABSTRACT FROM AUTHOR]

The transformer model is a famous natural language processing model proposed by Google in 2017. Now, with the extensive development of deep learning, many natural language processing tasks can be solved by deep learning methods. After the BERT model was proposed, many pre-trained models such as the XLNet model, the RoBERTa model, and the ALBERT model were also proposed in the research community. These models perform very well in various natural language processing tasks. In this paper, we describe and compare these well-known models. In addition, we also apply several types of existing and well-known models which are the BERT model, the XLNet model, the RoBERTa model, the GPT2 model, and the ALBERT model to different existing and well-known natural language processing tasks, and analyze each model based on their performance. There are a few papers that comprehensively compare various transformer models. In our paper, we use six types of well-known tasks, such as sentiment analysis, question answering, text generation, text summarization, name entity recognition, and topic modeling tasks to compare the performance of various transformer models. In addition, using the existing models, we also propose ensemble learning models for the different natural language processing tasks. The results show that our ensemble learning models perform better than a single classifier on specific tasks. [ABSTRACT FROM AUTHOR]

Abstractive summarization (AS) aims to generate more flexible and informative descriptions than extractive summarization. Nevertheless, it often distorts or fabricates facts in the original article. To address this problem, some existing approaches attempt to evaluate or verify factual consistency, or design models to reduce factual errors. However, most of the efforts either have limited effects or result in lower rouge scores while reducing factual errors. In other words, it is challenging to promote factual consistency while maintaining the informativeness of generated summaries. Inspired by the knowledge graph embedding technique, in this paper, we propose a novel cross-modal knowledge guided model (CKGM) for AS, which embeds a multimodal knowledge graph (MKG) combining image entity-relationship information and textual factual information (FI) into BERT to accomplish cross-modal information interaction and knowledge expansion. The pre-training method obtains rich contextual semantic information, while the knowledge graph supplements the textual information. In addition, an entity memory embedding algorithm is further proposed to improve information fusion efficiency and model training speed. We elaborately conducted ablation experiments and evaluated our model on the Visual Genome, FewRel, MSCOCO, and CNN/DailyMail datasets. Experimental results demonstrate that our model can significantly improve the FI consistency and informativeness of generated summaries. [ABSTRACT FROM AUTHOR]

Since the last decade, high-dimensional data has been increasing in various document mining fields, such as text summarization, text clustering, and text classification. The curse of dimensionality has an impact on the classification model's performance. The feature selection strategy is extremely effective in dealing with the curse of dimensionality issue. In this work, we present the Tom and Jerry Optimization technique(TJO) for feature subset selection. The proposed work uses the classifier error rate and the feature chosen rate to measure the candidate's fitness. The performance of the proposed scheme is examined using two popular benchmark text corpus and compared with five metaheuristic approaches. The best success rate obtained by the proposed scheme is 95.77%, whereas the best precision is 0.9509, recall is 0.9577 and F1-score is 0.9541. According to the comparison results, the proposed feature subset selection scheme outperforms the standard strategy. [ABSTRACT FROM AUTHOR]

As the demand for edge services intensifies, text, being the most common type of data, has seen a significant expansion in data volume and an escalation in processing complexity. Furthermore, mobile edge computing (MEC) service systems often faces challenges such as limited computational capabilities and difficulties in data integration, requiring the development and implementation of more efficient and lightweight methodologies for text data processing. To swiftly extract and analysis vital information from MEC text data, an automatic generation scheme of multi-document text summarization based on knowledge graph is proposed in this paper, named KGCPN. For the text data from MEC devices and applications, the natural language processing technology is used to execute the data pre-processing steps, which transforms the MEC text data into a computationally tractable and semantically comprehensible format. Then, the knowledge graph of multi-document text is constructed by integrating the relationship paths and entity descriptions. The nodes and edges of the knowledge graph serve to symbolize the semantic relationships within the text, and the Graph Convolution Neural network (GCN) is used to understand the text and learn the semantic representation. Finally, a pointer-generator network model accepts the encoding information from GCN and automatically generate a general text summarization. The experimental results indicate that our scheme can effectively facilitate the smart pre-processing and integration of MEC data. [ABSTRACT FROM AUTHOR]

With the rapid and unprecedented growth of textual data in recent years, there is a remarkable need for automatic text summarization models to retrieve useful information from these large numbers of textual documents without human intervention within a reasonable time. Text summarization is commonly performed based on extractive and abstractive paradigms. Although different machine learning and deep learning based methods have been proposed for the task of text summarization during the last decades, they are still in their early steps of development and their potential has yet to be fully explored. Accordingly, a new summarization model is proposed in this paper which takes advantage of both extractive and abstractive text summarization models as a single unified model based on the strategy gradient of reinforcement learning. The proposed model also employs the combination of convolutional neural network and gated recurrent unit in both extraction and abstraction modules besides attention mechanism. Moreover, language models, namely Word2Vec and BERT, are used as the backbone of the proposed model to better express sentence semantics as a word vector. We conducted our experiments on widely-studied text summarization datasets (CNN\Daily Mail and DUC-2004) and according to the empirical results, not only the proposed model achieved higher accuracy compared to both extractive and abstractive summarization models in terms of ROUGE metric but also its generated summaries presented higher saliency and readability based on human evaluation. [ABSTRACT FROM AUTHOR]

Peer reviews form an essential part of scientific communication. Scholarly peer review is probably the most accepted way to evaluate research papers by involving multiple experts to review the concerned research independently. Usually, the area chair, the program chair, or the editor takes a call weighing the reviewer's judgments. It communicates the decision to the author via writing a meta-review by summarizing the review comments. With the exponential rise in research paper submissions and the corresponding rise in the reviewer pool, it becomes stressful for the chairs/editors to manage conflicts, arrive at a consensus, and also write an informative meta-review. Here in this work, we propose a novel deep neural network-based approach for generating meta-reviews in an unsupervised fashion. To generate consistent meta-reviews, we use a generic template where the task is like to slot-fill the template with the generated meta-review text. We consider the setting where only peer reviews with no summaries or meta-reviews are provided and propose an end-to-end neural network model to perform unsupervised opinion-based abstractive summarization. We first use an aspect-based sentiment analysis model, which classifies the review sentences with the corresponding aspects (e.g., novelty, substance, soundness, etc.) and sentiment. We then extract opinion phrases from reviews for the corresponding aspect and sentiment labels. Next, we train a transformer model to reconstruct the original reviews from these extraction. Finally, we filter the selected opinions according to their aspect and/or sentiment at the time of summarization. The selected opinions of each aspect are used as input to the trained Transformer model, which uses them to construct an opinion summary. The idea is to give a concise meta-review that maximizes information coverage by focusing on aspects and sentiment present in the review, coherence, readability, and redundancy. We evaluate our model on the human written template-based meta-reviews to show that our framework outperforms competitive baselines. We believe that the template-based meta-review generation focusing on aspect and sentiment will help the editor/chair in decision-making and assist the meta-reviewer in writing better and more informative meta-reviews. We make our codes available at https://github.com/sandeep82945/Unsupervised-meta-review-generation. [ABSTRACT FROM AUTHOR]

Automatic text generation is the generation of natural language text by machines. Enabling machines to generate readable and coherent text is one of the most vital yet challenging tasks. Traditionally, text generation has been implemented either by using production rules of a predefined grammar or performing statistical analysis of existing human-written texts to predict sequences of words. Recently a paradigm change has emerged in text generation, induced by technological advancements, including deep learning methods and pre-trained transformers. However, many open challenges in text generation need to be addressed, including the generation of fluent, coherent, diverse, controllable, and consistent human-like text. This survey aims to provide a comprehensive overview of current advancements in automated text generation and introduce the topic to researchers by offering pointers and synthesis to pertinent studies. This paper studied the relevant twelve years of articles from 2011 onwards in the field of text generation and observed a total of 146 prime studies relevant to the objective of this survey that has been thoroughly reviewed and discussed. It covers core text generation applications, including text summarization, question–answer generation, story generation, machine translation, dialogue response generation, paraphrase generation, and image/video captioning. The most commonly used datasets for text generation and existing tools with their application domain have also been mentioned. Various text decoding and optimization methods have been provided with their strengths and weaknesses. For evaluating the effectiveness of the generated text, automatic evaluation metrices have been discussed. Finally, the article discusses the main challenges and notable future directions in the field of automated text generation for potential researchers. [ABSTRACT FROM AUTHOR]

Many enterprise systems are document-intensive and require extensive manual verification. The verification process has challenge in terms of time and remaining bugs. A general automatic or semi-automatic document verification system would be useful. However, as the nature of the natural language, the context is an important factor. In this research, the target context is selected to be the financial documents, which have been highly interested recently. An automatic document verification model based on only entities (mostly faced within financial documents) was experimented. The summary report was verified with original documents, such that entities in the summary were searched for matching in the original documents. Verification process success was evaluated by comparison of the named entity algorithms in the literature. The special Kaggle data set ready for this purpose was used for entity matching from the summary within the original documents. The average document verification accuracy of named entity finding algorithms for only financial type documents was 85.36%, where the proposed entity recognition algorithm reached 88.80%. On the other hand, the average document verification time of the experimented algorithms and the developed algorithm is 2.43 and 2.48 s respectively. As a conclusion, when both the BERT-base-cased classification model and rule-based approaches are applied specific to the context, it enhances the entity verification process with an insignificant time cost. Consequently, even we used limited data and rules, it is seen that there exists opportunity to automatize the document verification process with the support of both the BERT-base-cased classification model and rule-based approaches. [ABSTRACT FROM AUTHOR]

Unsupervised opinion summarization is the technique of automatically generates summaries without gold reference, and the summaries that reflects aspects of information about the entity. Although there are more mature studies on unsupervised opinion summarazaiton, but these studies focus more on unsupervised training methods and ignore the extraction of information by the model. In this paper, we propose JointSum, an unsupervised opinion summarization method based on variational autoencoder model. JointSum first extracts aspect and sentiment information in reviews by joint attention and dictionary learning, respectively. Joint attention consists of text attention and auxiliary attention, which can extract key information in the input text from different fine-grained levels. Then we calculate the variance and mean of the Gaussian distribution in variational autoencoder model using aspect and sentiment information. In addition, we added the review score prediction subtask to increase the robustness of the model. Finally, in generation phase, we adopt pointer-generator network because it includes copy and coverage mechanism that can solve problems in text generation. Experiments on Amazon and Yelp datasets, the results show that the model has good performance in both automatic and human evaluation, the ROUGE-L value on the Yelp dataset gets 20.83. [ABSTRACT FROM AUTHOR]

A document known as notitia criminis (NC) is use in the Brazilian Federal Police as the starting point of the criminal investigation. An NC aims to report a summary of investigative activities. Thus, it contains all relevant information about a supposed crime that occurred. To manage an inquiry and correlate similar investigations, the Federal Police usually needs to extract essential information from an NC document. The manual extraction (reading and understanding the entire content) may be human mentally exhausting, due to the size and complexity of the documents. In this light, natural language processing (NLP) techniques are commonly used for automatic information extraction from textual documents. Deep neural networks are successfully apply to many different NLP tasks. A neural network model that leveraged the results in a wide range of NLP tasks was the BERT model—an acronym for Bidirectional Encoder Representations from Transformers. In this article, we propose approaches based on the BERT model to extract relevant information from textual documents using automatic text summarization techniques. In other words, we aim to analyze the feasibility of using the BERT model to extract and synthesize the most essential information of an NC document. We evaluate the performance of the proposed approaches using two real-world datasets: the Federal Police dataset (a private domain dataset) and the Brazilian WikiHow dataset (a public domain dataset). Experimental results using different variants of the ROUGE metric show that our approaches can significantly increase extractive text summarization effectiveness without sacrificing efficiency. [ABSTRACT FROM AUTHOR]

Automatic summarization is attracting increasing attention as one of the most promising research areas. This technology has been tried in various real-world applications in recent years and achieved a good response. However, the applicability of conventional evaluation metrics cannot keep up with rapidly evolving summarization task formats and ensuing indicator. After recent years of research, automatic summarization task requires not only readability and fluency, but also informativeness and consistency. Diversified application scenarios also bring new challenges both for generative language models and evaluation metrics. In this review, we analysis and specifically focus on the difference between the task format and the evaluation metrics. [ABSTRACT FROM AUTHOR]

Due to the exponential growth of online information, the ability to efficiently extract the most informative content and target specific information without extensive reading is becoming increasingly valuable to readers. In this paper, we present 'EXABSUM,' a novel approach to Automatic Text Summarization (ATS), capable of generating the two primary types of summaries: extractive and abstractive. We propose two distinct approaches: (1) an extractive technique (EXABSUMExtractive), which integrates statistical and semantic scoring methods to select and extract relevant, non-repetitive sentences from a text unit, and (2) an abstractive technique (EXABSUMAbstractive), which employs a word graph approach (including compression and fusion stages) and re-ranking based on keyphrases to generate abstractive summaries using the source document as an input. In the evaluation conducted on multi-domain benchmarks, EXABSUM outperformed extractive summarization methods and demonstrated competitiveness against abstractive baselines. [ABSTRACT FROM AUTHOR]

The number of research articles published on COVID-19 has dramatically increased since the outbreak of the pandemic in November 2019. This absurd rate of productivity in research articles leads to information overload. It has increasingly become urgent for researchers and medical associations to stay up to date on the latest COVID-19 studies. To address information overload in COVID-19 scientific literature, the study presents a novel hybrid model named CovSumm, an unsupervised graph-based hybrid approach for single-document summarization, that is evaluated on the CORD-19 dataset. We have tested the proposed methodology on the scientific papers in the database dated from January 1, 2021 to December 31, 2021, consisting of 840 documents in total. The proposed text summarization is a hybrid of two distinctive extractive approaches (1) GenCompareSum (transformer-based approach) and (2) TextRank (graph-based approach). The sum of scores generated by both methods is used to rank the sentences for generating the summary. On the CORD-19, the recall-oriented understudy for gisting evaluation (ROUGE) score metric is used to compare the performance of the CovSumm model with various state-of-the-art techniques. The proposed method achieved the highest scores of ROUGE-1: 40.14%, ROUGE-2: 13.25%, and ROUGE-L: 36.32%. The proposed hybrid approach shows improved performance on the CORD-19 dataset when compared to existing unsupervised text summarization methods. [ABSTRACT FROM AUTHOR]

Creating a summarized version of a text document that still conveys precise meaning is an incredibly complex endeavor in natural language processing (NLP). Abstract text summarization (ATS) is the process of using facts from source sentences and merging them into concise representations while maintaining the content and intent of the text. Manually summarizing large amounts of text are challenging and time-consuming for humans. Therefore, text summarization has become an exciting research focus in NLP. This research paper proposed an ATS model using a Transformer Technique with Self-Attention Mechanism (T2SAM). The self-attention mechanism is added to the transformer to solve the problem of coreference in text. This makes the system to understand the text better. The proposed T2SAM model improves the performance of text summarization. It is trained on the Inshorts News dataset combined with the DUC-2004 shared tasks dataset. The performance of the proposed model has been evaluated using the ROUGE metrics, and it has been shown to outperform the existing state-of-the-art baseline models. The proposed model gives the training loss minimum to 1.8220 from 10.3058 (at the starting point) up to 30 epochs, and it achieved model accuracy 48.50% F1-Score on both the Inshorts and DUC-2004 news datasets. [ABSTRACT FROM AUTHOR]

Nowadays, an enormous amount of data is available on the WWW and exponentially growing as well. A lot of users use social networking websites such as Twitter, Facebook, Instagram, and Google+ as common platforms for sharing and exchanging views and opinions on any topics/events. The researchers have considered the reviews and views of the users on these platforms for sentiment analysis, opinion mining, question answering, text summarization, etc. The paper proposes a novel approach for identifying reviews or opinion of users having sarcasm in the text patterns at the clause level. The sentences are classified into four categories such as Simple Sentences, Compound Sentences, Complex Sentences, and Compound-Complex Sentences based on the rules derived from a decision tree. The Simple Sentences and Complex Sentences alone are considered for analysing the sentence patterns where a positive sentiment contrasts with a negative polarity and vice-versa. The decision tree and neuro-fuzzy rules are used on sentence structures to classify the sentences into sarcastic and non-sarcastic sentence patterns. Membership functions are used to map the fuzzy rules and linguistic grading is used for grading the sarcastic patterns. The proposed approach is evaluated on Twitter Dataset and found that the results are promising with the recent and relevant work. [ABSTRACT FROM AUTHOR]

With the rapid growth of social media platforms, digitization of official records, and digital publication of articles, books, magazines, and newspapers, lots of data are generated every day. This data is a foundation of information and contains a vast amount of text that may be complex, ambiguous, redundant, irrelevant, and unstructured. Therefore, we require tools and methods that can help us understand and automatically summarize the vast amount of generated text. There are mainly two types of approaches to perform text summarization: abstractive and extractive. In Abstractive Text Summarization, a concise summary is generated by including the salient features of the input documents and paraphrasing documents using new sentences and phrases. While in Extractive Text Summarization, a summary is produced by selecting and combining the most significant sentences and phrases from the source documents. The researchers have given numerous techniques for both kinds of text summarization. In this work, we classify Extractive Text Summarization approaches and review them based on their characteristics, techniques, and performance. We have discussed the existing Extractive Text Summarization approaches along with their limitations. We also classify and discuss evaluation measures and provide the research challenges faced in Extractive Text Summarization. [ABSTRACT FROM AUTHOR]

Due to the exponential overflow of textual information in various fields of knowledge and on the internet, it is very challenging to extract important information or to generate a summary from some multi-document collection in a specific field. With such a gigantic amount of textual content, human text summarization becomes impractical since it is expensive and consumes a lot of time and effort. So, developing automatic text summarization (ATS) systems is becoming increasingly essential. ATS approaches are either extractive or abstractive. The extractive approach is simpler and faster than the abstractive approach. This work proposes an extractive ATS system that aims to extract a small subset of sentences from a large multi-document text. First, the whole text is preprocessed by applying some natural language processing techniques such as sentences segmentation, words tokenization, removal of stop-words, and stemming to provide a structured representation of the original document collection. Based on this structured representation, the ATS problem is formulated as a multi-objective optimization (MOO) problem that optimizes the extracted summary to maintain the coverage of the main text content while avoiding redundant information. Secondly, an evolutionary sparse multi-objective algorithm is developed to solve the formulated large-scale MOO. The output of this algorithm is a set of non-dominated summaries (Pareto front). A novel criterion is proposed to select the target summary from the Pareto front. The proposed ATS system has been examined using (DUC) datasets, and the output summaries have been evaluated using (ROUGE) metrics and compared with the literature. [ABSTRACT FROM AUTHOR]

Over the past few years, the explainable artificial intelligence (XAI) model receives a broad desire for investigation. The natural language processing (NLP) commune is reaching the fundamental change too – constructing a set of paradigms, which describe the preference on a few chief jobs devoid of influencing the execution. Abstractive Text Summarization (ATS) remains the job of building summary sentences by fusing factualities out of disparate source sentences and compressing them into a smaller portrayal when sustaining data content and comprehensive sense. This remains extremely arduous and long-drawn-out for people to physically summarize huge text documents. This study proffers Ontology-based Knowledge Aware Multi-focus Conditional Generative Adversarial Network (OKAM-CGAN) for novel documents. This could build novel sentences by analyzing many finer pieces than sentences, especially, semantic phrases. The proffered OKAM-CGAN comprises 3 prime portions – ontology aware knowledge-based document representation module, multitask and multi-focus learning unit, and an adversarial network unit. Experiential assessment is performed by correlating with advanced methodologies like RNN-W, CopyNet, GCU, Seq2Seq, and KESG concerning ROUGE scores. Consequently, it is observed that the proffered OKAM-CGAN attains 42.1% of ROUGE-L, 40% of accuracy, 45%of precision, and 53% of recall for the CNN/Daily Mail database and 45% of ROUGE-L, 4% of accuracy, 54% of precision, and 57% of recall for the Edmunds database. [ABSTRACT FROM AUTHOR]

Recently, for the abstractive summarization task, most of proposed techniques have adopted the deep recurrent neural network (RNN)-based sequential auto-encoding architecture to effectively learn and generate meaningful summaries for different input documents. However, most of recent RNN-based models always suffer the challenges related to the involvement of much capturing high-frequency/reparative phrases in long documents during the training process which leads to the outcome of trivial and generic summaries are generated. In addition, the lack of thorough analysis on the sequential and long-range dependency relationships between words within different contexts while learning the textual representation also makes the achieved summaries unnatural and incoherent. In order to deal with these challenges, in this paper we proposed a novel semantic-enhanced generative adversarial network (GAN)-based approach for abstractive text summarization task, called as: SGAN4AbSum. We use an adversarial training strategy for our text summarization model which trains the generator and discriminator to simultaneously handle the summary generation and distinguishing the generated summary with the ground-truth one. The input of generator is the jointed rich-semantic and global structural latent representations of training documents which are achieved by applying a combined BERT and graph convolutional network textual embedding mechanism. Extensive experiments in benchmark datasets demonstrate the effectiveness of our proposed SGAN4AbSum which achieve the competitive ROUGE-based scores in comparing with state-of-the-art abstractive text summarization baselines. [ABSTRACT FROM AUTHOR]

The availability of a tremendous amount of online information bringing about a broad interest in extracting relevant information in a compact and meaningful way, prompted the need for automatic text summarization. Hence, in the proposed system, the automated text summarization has been considered as an extractive single-document summarization problem, and a Cat Swarm Optimization (CSO) algorithm-based approach is proposed to solve it, whose objective is to generate good summaries in terms of content coverage, informative, anti-redundancy, and readability. In this work, input documents are pre-processed first. Then the cat population is initialized, where each individual (cat) in a binary vector is randomly initialized in the search space, considering the constraint. The objective function is then formulated considering different sentence quality measures. The Best Cat Memory Pool (BCMP) is initialized based on the objective function score. After that, individuals are randomly distributed for position updating to perform seeking/tracing mode operations based on the mixture ratio in each iteration. BCMP is also updated accordingly. Finally, an optimal individual is chosen to generate the summary after the last iteration. DUC-2001 and DUC-2002 data sets and ROUGE measures are used for system evaluation, and the obtained results are compared with the various state-of-the-art methods. We have achieved approximately 25% and 5% improvement on ROUGE-1 and ROUGE-2 scores on the datasets over the best existing method mentioned in this paper, revealing the proposed method's superiority. The proposed system is also evaluated considering the generational distance, CPU processing time, cohesion, and readability factor, reflecting that the system-generated summaries are readable, concise, relevant, and fast. We have also conducted a two-sample t-test, and one-way ANOVA test showing the proposed approach is statistically significant. [ABSTRACT FROM AUTHOR]

In today's world as the data on the web is increasing it becomes a challenge to identify the relevant information. Automatic text summarization (ATS) provides a significant answer to it. In this paper, fuzzy logic and shark smell optimization (SSO) based algorithm for extractive text summarization is proposed. Shark Smell Optimization has been used to assign a weight to eight different features to identify the less and more important text features of text summarization. Then, the Fuzzy Logic's inference system is utilized to generate fuzzy rules, and finally an automated summary is generated. The system generated summaries have been tested against the reference summaries from the DUC 2002, DUC 2003, DUC-2004 and TAC-11 dataset and ROUGE toolkit has been used for the evaluation of the proposed solution. Results of the proposed algorithm are compared against traditional methods and the rouge score suggested that the proposed algorithm generates better results than other methods. [ABSTRACT FROM AUTHOR]

Automatic legal text summarization becomes a challenging process because of unusual structure and high complexity of the documents. Existing works related to legal text summarization are available, both for general text and a few targeted in summarizing legal documents, however, they relied on massive quantity of labelled dataset by the use of hand-engineered features, leveraging on domain knowledge and focused their attention on a narrow sub-domain for increased effectiveness. To resolve this issue, this paper presents an automated optimal DL based legal text summarization (ODL-LTS) approach. Initially, the proposed ODL-LTS technique performs similarity measurement using TF-IDF and Similarity based on Rouge-L scores (SROUGE). Besides, glowworm swarm optimization (GSO) with bidirectional gated recurrent neural network (BiGRNN) model is employed for summary generation, shows the novelty of the work. The major benefit of the ODL-LTS model is that it does not depend upon handcrafted features or domain specific knowledge, nor is their application limited to specific sub-domains thus making them suitable to be extended to other domains as well. The performance validation of the ODL-LTS technique takes place using our own dataset and the experimental results demonstrates the prosminig performance with the precision of 35.54%, recall of 51.60%, and F-score of 39.06%. [ABSTRACT FROM AUTHOR]

With the emergence of deep learning, the attention of researchers has increased significantly towards abstractive text summarization approaches. Though extractive text summarization (ETS) is an important approach, the generated summaries are not always coherent. This paper mainly focuses on the abstractive text summarization (ATS) approach for Telugu language to generate coherent summary. The majority research on ATS approach is conducted in English, while no significant research in Telugu has been documented. An abstractive Telugu text summarization model based on sequence-to-sequence (seq2seq) encoder-decoder architecture is proposed in this paper. The seq2seq model is implemented with bidirectional long short-term memory (Bi-LSTM) based encoder and long short-term memory (LSTM) based decoder. The existing ATS approaches have some drawbacks such as they cannot handle out vocabulary words, attention deficiency issue arising while handling long text sequence and repetition problem. To overcome these issues, some operating mechanisms like pointer generator network, temporal attention mechanism and coverage mechanism are also integrated in the proposed model. Besides, diverse beam search decoding algorithm is also employed to increase the diversity of generated summary. Thus, the proposed seq2seq model is the combination of Bi-LSTM and LSTM based encoder-decoder, pointer generator network, temporal attention mechanism, coverage mechanism and diverse beam search decoding algorithm. The performance of the proposed work is evaluated using the ROUGE toolkit in terms of F-measure, recall and precision. The experimental results of the proposed scheme are evaluated with other existing methods to show that the proposed ATS model outperforms existing Telugu text summarization models. [ABSTRACT FROM AUTHOR]

Text summarization is the process of converting the input document into a short form, provided that it preserves the overall meaning associated with it. Primarily, text summarization is achieved in two ways, i.e., abstractive and extractive. Extractive summarizers select a few best sentences out of the input document, while abstractive methods may modify the sentence structure or introduce new sentences. The proposed approach is an extractive text summarization technique, where we have expanded topic modeling specifically to be applied to multiple lower-level specialized entities (i.e., groups) embedded in a single document. Our goal is to overcome the lack of coherence issues found in the summarization techniques. Topic modeling was initially proposed to model text data at the multi-document and word levels without considering sentence modeling. Subsequently, it has been applied at the sentence level and used for the document summarization; however, certain limitations were associated. Topic modeling does not perform as expected when applied to a single document at the sentence level. To address this shortcoming, we have proposed a summarization approach that is incorporated at the individual document and clusters level (instead of the sentence level). We aim to choose the best statement from each group (containing sentences of the same kind) found in the given text. We have tried to select the perfect topic by evaluating the probability distribution of the words and respective topics' at the cluster level. The method is evaluated on two standard datasets and shows significant performance gains over existing text summarization techniques. Compared to other text summarization techniques, the Rouge parameters for automatic evaluation show a considerable improvement in F-measure, precision, and recall of the generated summary. Furthermore, a manual evaluation has demonstrated that the proposed approach outperforms the current state-of-the-art text summarization approaches. [ABSTRACT FROM AUTHOR]

Considering the huge volume of opinion texts published on various social networks, it is extremely difficult to peruse and use these texts. The automatic creation of summaries can be a significant help for the users of such texts. The current paper employs manifold learning to mitigate the challenges of the complexity and high dimensionality of opinion texts and the K-Means algorithm for clustering. Furthermore, summarization based on the concepts of the texts can improve the performance of the summarization system. The proposed method is unsupervised extractive, and summarization is performed based on the concepts of the texts using the multi-objective pruning approach. The main parameters utilized to perform multi-objective pruning include relevancy, redundancy, and coverage. The simulation results show that the proposed method outperformed the MOOTweetSumm method while providing an improvement of 11% in terms of the ROGUE-1 measure and an improvement of 9% in terms of the ROGUE-L measure. [ABSTRACT FROM AUTHOR]

During the outbreak of a specific social event, end-to-end automatic opinion summarization is needed to analyze the surge of text related to the event. However, in the Chinese domain, the major existing works either emphasize salient aspects or sentence extraction in a discrete fashion with no consideration of human readability, or focus on short Chinese text. To remedy the drawbacks of these methods, in this paper, an event-based opinion summarization model for long Chinese text with a parameter fusion mechanism is proposed to address the human readability and imbalance issue of the event-based datasets. In particular, to capture the sentiment information in the source article in an end-to-end manner, a sentiment attention layer and a sentiment cross-entropy loss function are presented. In addition, when facing the issue of imbalance in event-based datasets, a parameter fusion mechanism inspired by the federated learning is proposed, which can further improve the human readability of the output. Finally, the efficacy of the proposed model is substantiated via comprehensive experiments performed on the collected event-based datasets, the Chinese long text summarization dataset (CLTS), and the cable news network/daily mail (CNN/DM) dataset using Recall-Oriented Understudy for Gisting Evaluation (ROUGE) and sentiment classification accuracy metrics. In addition, the source code is made available at https://github.com/ShawnYoung97/opinion-sum. [ABSTRACT FROM AUTHOR]

In the past few decades, the automatic text summarization process has become an essential area in research. The automatic text summarization process extracts the essential and the most-resourceful information the users need, and various users in many fields easily handle it. This work extracts linear and non-linear information using a Support Vector Machine (SVM). Then, the extracted features are given into the Bag of Features (BoF) which is provided as an input to the classifier model known as Multi-layered CNN for feature representation. This process is known as BoF-CNN. The multiple layers of the CNN model analyse the given features and provide the weighted score for the keywords based on the higher needs. The importance/classification is provided. The combinations of features are weighted with the graph model when placed in BoF. The proposed model effectively analyses the features and classifies the text based on weight. The simulation is done in MATLAB 2020a environment and provides better results when analysing incoming data. [ABSTRACT FROM AUTHOR]

The difficulty of deriving value out of vast available scientific literature in a condensed form lead us to look for a proficient theme based summarization solution which can preserve precise biomedical content. The study targets to analyze impact of combining semantic biomedical concepts extraction, frequent item-set mining and clustering techniques over information retention, objective functions and ROUGE values for the obtained final summary. The suggested frequent item-set mining and clustering (FRI-CL) graph-based framework uses UMLS metathesarus and BERT-based semantic embeddings to identify domain-relevant concepts. The scrutinized concepts are mined according to their relationship with neighbors and frequency via an amended FP-Growth model. The framework utilizes S-DPMM clustering, which is a probabilistic mixture model and aids in the identification and clubbing of complex relevant patterns to increase coverage of important sub-themes. The sentences with the frequent concepts are scored via PageRank to form an efficient and compelling summary. The research experiments on the 100 sample biomedical documents taken from PubMed archives are evaluated via calculation of ROUGE scores, coverage, readability, non-redundancy, memory utilization and information retention from the summary output. The results with the FRI-CL summarization system showcased 10% ROUGE performance improvement and are at par with the other baseline methods. On an average 30–40% improvement in memory utilization is observed with up to 50% information retention when experiments are performed using S-DPMM clustering. The research indicates that the fusion of semantic mapping, clustering, along with frequent-item set mining of biomedical concepts enhance the overall co-related information covering all sub-themes. [ABSTRACT FROM AUTHOR]

Keyphrases are crucial for searching and systematizing scholarly documents. Most current methods for keyphrase extraction are aimed at the extraction of the most significant words in the text. But in practice, the list of keyphrases often includes words that do not appear in the text explicitly. In this case, the list of keyphrases represents an abstractive summary of the source text. In this paper, we experiment with popular transformer-based models for abstractive text summarization using four benchmark datasets for keyphrase extraction. We compare the results obtained with the results of common unsupervised and supervised methods for keyphrase extraction. Our evaluation shows that summarization models are quite effective in generating keyphrases in the terms of the full-match F1-score and BERTScore. However, they produce a lot of words that are absent in the author's list of keyphrases, which makes summarization models ineffective in terms of ROUGE-1. We also investigate several ordering strategies to concatenate target keyphrases. The results showed that the choice of strategy affects the performance of keyphrase generation. [ABSTRACT FROM AUTHOR]

Text summarization is an important task in natural language processing and it has been applied in many applications. Recently, abstractive summarization has attracted many attentions. However, the traditional evaluation metrics that consider little semantic information, are unsuitable for evaluating the quality of deep learning based abstractive summarization models, since these models may generate new words that do not exist in the original text. Moreover, the out-of-vocabulary (OOV) problem that affects the evaluation results, has not been well solved yet. To address these issues, we propose a novel model called ENMS, to enhance existing N-gram based evaluation metrics with semantics. To be specific, we present two types of methods: N-gram based Semantic Matching (NSM for short), and N-gram based Semantic Similarity (NSS for short), to improve several widely-used evaluation metrics including ROUGE (Recall-Oriented Understudy for Gisting Evaluation), BLEU (Bilingual Evaluation Understudy), etc. NSM and NSS work in different ways. The former calculates the matching degree directly, while the latter mainly improves the similarity measurement. Moreover we propose an N-gram representation mechanism to explore the vector representation of N-grams (including skip-grams). It serves as the basis of our ENMS model, in which we exploit some simple but effective integration methods to solve the OOV problem efficiently. Experimental results over the TAC AESOP dataset show that the metrics improved by our methods are well correlated with human judgements and can be used to better evaluate abstractive summarization methods. [ABSTRACT FROM AUTHOR]

Due to the exponential growth in the number of documents on the Web, accessing the salient information relevant to a user need is gaining importance, which increases the popularity of text summarization. Recent progress in deep learning shifted the research in text summarization from extractive methods towards more abstractive approaches. The research and the available resources remain mostly limited to the English language, which prevents progress in other languages. There is need in low-resourced languages for gathering large-scale resources suitable for such tasks. In this study, we release two large-scale datasets (TR-News and HU-News) that can serve as benchmarks in the abstractive summarization task for Turkish and Hungarian. The datasets are primarily compiled for text summarization, but are also suitable for other tasks such as topic classification, title generation, and key phrase extraction. Morphology is important for these agglutinative languages since meaning is carried mostly within the morphemes of the words. We utilize these morphological properties for tokenization to retain the semantic information and reduce the vocabulary sparsity introduced by the agglutinative nature of these languages. Using the datasets compiled, we propose linguistically-oriented tokenization methods (SeparateSuffix and CombinedSuffix) and evaluate them on the state-of-the-art abstractive summarization models. The SeparateSuffix method achieves the highest ROUGE-1 score on the TR-News dataset and provides promising results on the HU-News dataset. In another experiment, we show that the multilingual cased BERT model outperforms monolingual BERT models for both languages and reaches the highest ROUGE-1 score on the HU-News dataset. Lastly, we provide qualitative analysis of the generated summaries on the TR-News dataset. [ABSTRACT FROM AUTHOR]