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Representation transfer is a widely used technique in natural language processing. We propose methods of cleaning the dominant dataset of text simplification (TS) WikiLarge in multi-views to remove errors that impact model training and fine-tuning. The results show that our method can effectively refine the dataset. We propose to take the pre-trained text representations from a similar task (e.g., text summarization) to text simplification to conduct a continue-fine-tuning strategy to improve the performance of pre-trained models on TS. This approach will speed up the training and make the model convergence easier. Besides, we also propose a new decoding strategy for simple text generation. It is able to generate simpler and more comprehensible text with controllable lexical simplicity. The experimental results show that our method can achieve good performance on many evaluation metrics. • Using sentence representations to refine the dataset. • Fine-tuning by taking text representations from similar tasks. • A new decoding strategy for simple text generation. [ABSTRACT FROM AUTHOR]

As free online encyclopedias with massive volumes of content, Wikipedia and Wikidata are key to many Natural Language Processing (NLP) tasks, such as information retrieval, knowledge base building, machine translation, text classification, and text summarization. In this paper, we introduce WikiDes, a novel dataset to generate short descriptions of Wikipedia articles for the problem of text summarization. The dataset consists of over 80k English samples on 6987 topics. We set up a two-phase summarization method — description generation (Phase I) and candidate ranking (Phase II) — as a strong approach that relies on transfer and contrastive learning. For description generation, T5 and BART show their superiority compared to other small-scale pre-trained models. By applying contrastive learning with the diverse input from beam search, the metric fusion-based ranking models outperform the direct description generation models significantly up to ≈ 22 ROUGE in topic-exclusive split and topic-independent split. Furthermore, the outcome descriptions in Phase II are supported by human evaluation in over 45.33% chosen compared to 23.66% in Phase I against the gold descriptions. In the aspect of sentiment analysis, the generated descriptions cannot effectively capture all sentiment polarities from paragraphs while doing this task better from the gold descriptions. The automatic generation of new descriptions reduces the human efforts in creating them and enriches Wikidata-based knowledge graphs. Our paper shows a practical impact on Wikipedia and Wikidata since there are thousands of missing descriptions. Finally, we expect WikiDes to be a useful dataset for related works in capturing salient information from short paragraphs. The curated dataset is publicly available at: https://github.com/declare-lab/WikiDes. • Wikipedia- and Wikidata-based dataset to generate short descriptions from paragraphs. • Two-phase summarization — candidate description generation and ranking. • Sentiment consistency analysis between paragraph and gold description. [ABSTRACT FROM AUTHOR]

The enormous quantity of text makes it challenging for users to obtain the key information and knowledge. Automatic text summarization can alleviate this problem by providing reliable summaries for massive text documents. During the last decade, significant achievements have been made in text summarization. We conduct this survey to explore what research community is focused on, the application scenarios of summarization, the state-of-the-art techniques and methods, and to analyze the challenges and future direction. We summarize that incorporating with natural language processing, previous text summarization research applied knowledge-based methods, graph-based methods, statistical learning methods, and deep learning methods. Applying large language model to text summarization is still in its early stages. By analyzing current research progress, we conclude that understand semantic information and specific domain knowledge is required for text summarization, and the conciseness and readability of the summary should be ensured. The future research opportunity is automatic knowledge summarization, and more research effort is urgently needed to explore. • Significant achievements of automatic text summarization have been made. • We aim to explore the research community of automatic text summarization. • Incorporating with NLP, text summarization applied knowledge-based, graph-based and machine learning methods. • The combination of extractive and abstractive strategies is required, which can ensure the quality of the summary. • The future research opportunity is automatic knowledge summarization. [ABSTRACT FROM AUTHOR]

Specifically focusing on the landscape of abstractive text summarization, as opposed to extractive techniques, this survey presents a comprehensive overview, delving into state-of-the-art techniques, prevailing challenges, and prospective research directions. We categorize the techniques into traditional sequence-to-sequence models, pre-trained large language models, reinforcement learning, hierarchical methods, and multi-modal summarization. Unlike prior works that did not examine complexities, scalability and comparisons of techniques in detail, this review takes a comprehensive approach encompassing state-of-the-art methods, challenges, solutions, comparisons, limitations and charts out future improvements — providing researchers an extensive overview to advance abstractive summarization research. We provide vital comparison tables across techniques categorized — offering insights into model complexity, scalability and appropriate applications. The paper highlights challenges such as inadequate meaning representation, factual consistency, controllable text summarization, cross-lingual summarization, and evaluation metrics, among others. Solutions leveraging knowledge incorporation and other innovative strategies are proposed to address these challenges. The paper concludes by highlighting emerging research areas like factual inconsistency, domain-specific, cross-lingual, multilingual, and long-document summarization, as well as handling noisy data. Our objective is to provide researchers and practitioners with a structured overview of the domain, enabling them to better understand the current landscape and identify potential areas for further research and improvement. [Display omitted] • Overview of state-of-the-art techniques in abstractive text summarization. • Comparative analysis of models in abstractive summarization. • Identification of challenges and potential improvements in the field. • Exploration of future research directions and emerging frontiers. • Holistic survey of abstractive text summarization. [ABSTRACT FROM AUTHOR]

Automatic Text Summarization (ATS) involves estimating the salience of information and creating coherent summaries that include all relevant and important information from the original text. Extensive research has been carried out on ATS since 1958, gradually evolving from simple to advanced techniques, including machine learning-based, neural network-based, and deep learning-based techniques. Progress has been made in both extractive and abstractive methods throughout this development. Despite numerous surveys on ATS, there remains a notable absence of a comprehensive literature review encompassing the latest advancements in deep learning techniques for text summarization. Therefore, this paper provides a Systematic Literature Review (SLR) of deep learning-based text summarization in both types (extractive and abstractive) between 2014 and 2023. To the best of our knowledge, this is the first SLR that offers a comprehensive overview of extractive and abstractive text summarization techniques based on Deep Learning models. According to the defined inclusion and exclusion criteria, 73 deep learning-based text summarization studies are selected for further investigation. The structure of the review is organized as follows. Firstly, it identifies and examines the deep learning models employed in both extractive and abstractive text summarization. Then, the input text's representation methods are identified and discussed clearly. Next, training strategies used in supervised extractive summarization techniques are identified. Furthermore, mechanisms that improve the abstractive summarization process are identified. Additionally, the most commonly used datasets and their advantages and disadvantages are discussed. The most commonly used evaluation metrics are also identified. Finally, the challenges and possible solutions to guide future research in the field are discussed. • Analyze the latest deep learning-based text summarization techniques. • Introduce a new taxonomy of deep learning models for text summarization. • Identify methods for representing input text clearly. • Identify training strategies for supervised extractive summarization techniques. • Identify the mechanisms used in abstractive summarization techniques. [ABSTRACT FROM AUTHOR]

Assigning International Classification of Diseases (ICD) codes to clinical texts is a common and crucial practice in patient classification, hospital management, and further statistics analysis. Current auto-coding methods mainly transfer this task to a multi-label classification problem. Such solutions are suffering from high-dimensional mapping space and excessive redundant information in long clinical texts. To alleviate such a situation, we introduce text summarization methods to the ICD coding regime and apply text matching to select ICD codes. We focus on the tenth revision of the ICD (ICD-10) coding and design a novel summarization-based approach (SuM) with an end-to-end strategy to efficiently assign ICD-10 code to clinical texts. In this approach, a knowledge-guided pointer network is purposed to distill and summarize key information in clinical texts precisely. Then a matching model with matching-aggregation architecture follows to align the summary result with code, tuning the one-vs-all scenario to one-vs-one matching so that the large-label-space obstacle laid in classification approaches would be avoided. The 12,788 ICD-10 coded discharge summaries from a Chinese hospital were collected to evaluate the proposed approach. Compared with existing methods, the purposed model achieves the greatest coding results with Micro AUC of 0.9548, MRR@10 of 0.7977, Precision@10 of 0.0944, and Recall@10 of 0.9439 for the TOP-50 Dataset. Results on the FULL-Dataset remain consistent. Also, the proposed knowledge encoder and applied end-to-end strategy are proven to facilitate the whole model to gain efficacy in selecting the most suitable code. The proposed automatic ICD-10 code assignment approach via text summarization can effectively capture critical messages in long clinical texts and improve the performance of ICD-10 coding of clinical texts. • The text summarization method is used for ICD-10 coding with better distillation of key information in long clinical texts. • With the guidance of the main diagnosis, the summary of the long clinical text is precise and applicable for code matching. • The text matching method facilitates ICD-10 coding with interactive features and the one-vs-one operation. • A novel end-to-end model of the summarization module and text matching module is proposed. [ABSTRACT FROM AUTHOR]

The ever-increasing volume of research literature poses challenges for researchers in keeping up with related works in their fields. Automating the generation of related work section holds promise for saving time and effort. However, current models often fall short of producing coherent and logically correct related work with multiple sentences, a phenomenon we refer to as rhetorical structure chaos. Rhetorical structure describes how adjacent spans of units are connected to each other, and logically correct rhetorical structure is essential for a well-structured related work. Hence, to tackle the rhetorical structure chaos issue, this paper explicitly incorporates rhetorical structure information into related work generation. Firstly, we conduct the first rhetorical structure analysis for related work section, which provides insights into understanding the organization and arrangement of contents within related work. Then, based on two preliminary studies on rhetorical structure, we present a novel related work generation model called RSGen, which incorporates rhetorical structure at both the encoding and decoding stages. The encoding stage is facilitated with a rhetorical structure-based graph encoder, while the decoding process is guided by a rhetorical plan — ordered sequence of rhetorical functions of the related work. We conduct extensive experiments on three related work generation datasets to evaluate the performance of our model. The results show that our approach achieves state-of-the-art performance on ROUGE metrics. An ablation study and more analyses further highlight the remarkable efficacy of introducing rhetorical structure into both the encoding and decoding stages. • The first rhetorical structure analysis for related work section. • A model enhanced with rhetorical structure at both encoding and decoding stages. • Superior performance that exceeds all baselines and achieves state-of-the-art. [ABSTRACT FROM AUTHOR]

Text summarization research is significant and challenging in the domain of natural language processing. Abstractive text summarization mainly uses the encoder-decoder framework, wherein the encoder component does not have a sufficient semantic comprehension of the input text, and there are exposure biases and semantic inconsistencies between the reference and generated summaries during the training process. We propose an improved encoder-decoder model that incorporates a hierarchical attention mechanism and multiobjective reinforcement learning. The encoder introduces a multihead self-attention mechanism to allow for the acquisition of more comprehensive semantic information from multiple angles and dimensions, while the decoder introduces a pointer-generator network to solve the out-of-vocabulary problem. Multiobjective reinforcement learning methods are constructed throughout the training process to optimize the model in terms of addressing exposure bias, maintaining semantic consistency, and enhancing readability. The results of the comparative experiments demonstrate that the proposed model significantly improved in terms of the ROUGE evaluation metric, and the generated summaries were semantically similar to the reference summaries. [ABSTRACT FROM AUTHOR]

Abstractive Text summarization aims to generate a short summary for a document while preserving salient information. Recently, contrastive learning has been extended from visual representation to summarization tasks. At present, the methods of contrastive learning summarization focus on modeling the global semantics of source documents, targets and candidate summaries to maximize their similarities. However, they ignore the influence of sentence semantics in the document. In this paper, we propose a sentence-level salience contrastive learning method to help the model capture salient information and denoise. The model expresses the sentence salience according to the semantic similarity between the summaries and sentences of the source document, and integrates the similarity distance into the contrastive loss in the form of soft weights. Therefore, our model maximize the similarity between summaries and salient information, while minimizing the similarity between summaries and potential noise. We have verified our method in three widely used datasets, CNN/Daily Mail, XSum and PubMed. The experimental results show that the proposed method can significantly improve the baseline performance and achieve competitive performance in the existing contrastive learning methods. • We propose a contrastive learning method for abstractive text summarization. • We construct contrastive samples in the source sentences. • We incorporate the salient distance-aware weightsinto the contrastive loss. • Experiments show that our model has obviously better performance. [ABSTRACT FROM AUTHOR]

Cyberbullying poses a digital threat to society. In this survey, we explain what cyberbullying is and its various forms. We focus on social media platforms and instant messaging apps that are susceptible to cyberbullying, discussing how we can identify such behavior in these spaces. Moving on, we conduct a systematic review of publicly available datasets in different languages, exploring techniques for data preprocessing, feature representation, and methodologies used in textual analysis for cyberbullying detection. We specifically look at natural language-based and platform-specific preprocessing methods. We also cover popular feature representation techniques like sentiment analysis, user information, text summarization, symbols, images, and word embedding for detecting cyberbullying. Next, we categorize existing techniques, including machine learning and neural networks, highlighting research gaps. Additionally, we discuss the challenges associated with current datasets and methods. This survey aims to provide early researchers with insights into cyberbullying literature and guide them in exploring potential research directions. • Analysis of prior literature for cyberbullying detection. • Proposes possible future research directions based on identified research challenges. • Identifies the need for high-quality datasets with contextual information. • Develop mitigation systems relevant in real-time. [ABSTRACT FROM AUTHOR]

The adoption of Deep Neural Networks (DNNs) has greatly benefited Natural Language Processing (NLP) during the past decade. However, the demands of long document analysis are quite different from those of shorter texts, while the ever increasing size of documents uploaded online renders automated understanding of lengthy texts a critical issue. Relevant applications include automated Web mining, legal document review, medical records analysis, financial reports analysis, contract management, environmental impact assessment, news aggregation, etc. Despite the relatively recent development of efficient algorithms for analyzing long documents, practical tools in this field are currently flourishing. This article serves as an entry point into this dynamic domain and aims to achieve two objectives. First of all, it provides an introductory overview of the relevant neural building blocks, serving as a concise tutorial for the field. Secondly, it offers a brief examination of the current state-of-the-art in two key long document analysis tasks: document classification and document summarization. Sentiment analysis for long texts is also covered, since it is typically treated as a particular case of document classification. Consequently, this article presents an introductory exploration of document-level analysis, addressing the primary challenges, concerns, and existing solutions. Finally, it offers a concise definition of "long text/document", presents an original overarching taxonomy of common deep neural methods for long document analysis and lists publicly available annotated datasets that can facilitate further research in this area. [ABSTRACT FROM AUTHOR]

Mathematical teaching techniques encompass a diverse range of approaches for students to grasp mathematical concepts. Currently, various mathematical teaching techniques exist, and it is crucial to extract personalized teaching techniques based on students' knowledge level. Cognitive Computing systems in the learning domain open a substantial range of possible alternatives, thereby helping both students and teachers. These systems enhance the human cognitive capabilities by developing cognitive assistant, which is a flexible and adaptive learning software and a continuous learning system. In this paper, a personalized cognitive layered framework is proposed to map the student's reasoning level with the mathematical teaching techniques. The analysis is done with real time data collected from the School for Grades 1 to 6 and mathematical teaching techniques extracted from mathematical research articles. The Hypothesis Evidence Extraction (HypoEE) algorithm is proposed to extract optimal teaching techniques. The proposed system follows the pipeline of cognitive computing processes such as extraction of factors of importance, knowledge assertion graph construction, node prioritization, correlation extraction, text summarization, hypothesis identification, evidence extraction and scoring, hypothesis refinement, inference and decision formulation. The system is evaluated using performance metrics and cognitive metrics. The validation is done by three mathematical subject experts. The results showcase a Confidence Weighted Score (CWS) of 0.96, an accuracy of 0.994, 0.92 precision, 0.88 recall, 0.89 F1 score, 0.83 specificity and cognitive metric scores indicating a taskability of 0.89, 0.86 knowledge capacity and 0.89 knowledge utilization. [Display omitted] • A personalized cognitive layered framework for deriving the most effective and personalized mathematical teaching strategy. • A cognitive system driven by evidence-based inference to personalize education. • A text summarization algorithm crafted to produce concise summaries from large volumes of text. • Application of the Hypothesis Evidence Extraction (HypoEE) algorithm to extract optimal mathematical teaching techniques based on individual reasoning levels. • Analyzing performance through expert assessments and cognitive evaluation metrics. [ABSTRACT FROM AUTHOR]

In this study, we conceptualize two approaches, model-based and text-based, grounded on mental models and discourse comprehension theories, to computerized summary analysis. We juxtapose the model-based approach with the text-based approach to explore shared knowledge dimensions and associated measures from both approaches and use them to examine changes in students' summaries over time. We used 108 cases in which we computed model-based and text-based measures for two versions of students' summaries (i.e., initial and final revisions), resulting in a total of 216 observations. We used correlations, Principal Components Analysis (PCA), and Linear Mixed-Effects models. This exploratory investigation suggested a shortlist of text-based measures, and the findings of the PCA demonstrated that both model-based and text-based measures explained the three-dimensional model (i.e., surface, structure, and semantic). Overall, model-based measures were better for tracking changes in the surface dimension, while text-based measures were descriptive of the structure dimension. Both approaches worked well for the semantic dimension. The tested text-based measures can serve as a cross-reference to evaluate students' summaries along with the model-based measures. The current study shows the potential of using multidimensional measures to provide formative feedback on students' knowledge structure and writing styles along the three dimensions. • This study introduces two types of Automated Summary Evaluation (ASE) approaches. • We explored shareable knowledge dimensions and their measures in both ASE approaches. • We employed PCA and Mixed-Effects models to assess 216 observations across 108 cases. • PCA showed measures from both approaches explained the three-dimensional model. • Both approaches' measures could enable multidimensional assessment and feedback. [ABSTRACT FROM AUTHOR]

Cyberbullying poses a digital threat to society. In this survey, we explain what cyberbullying is and its various forms. We focus on social media platforms and instant messaging apps that are susceptible to cyberbullying, discussing how we can identify such behavior in these spaces. Moving on, we conduct a systematic review of publicly available datasets in different languages, exploring techniques for data preprocessing, feature representation, and methodologies used in textual analysis for cyberbullying detection. We specifically look at natural language-based and platform-specific preprocessing methods. We also cover popular feature representation techniques like sentiment analysis, user information, text summarization, symbols, images, and word embedding for detecting cyberbullying. Next, we categorize existing techniques, including machine learning and neural networks, highlighting research gaps. Additionally, we discuss the challenges associated with current datasets and methods. This survey aims to provide early researchers with insights into cyberbullying literature and guide them in exploring potential research directions. • Analysis of prior literature for cyberbullying detection. • Proposes possible future research directions based on identified research challenges. • Identifies the need for high-quality datasets with contextual information. • Develop mitigation systems relevant in real-time. [ABSTRACT FROM AUTHOR]

Mathematical teaching techniques encompass a diverse range of approaches for students to grasp mathematical concepts. Currently, various mathematical teaching techniques exist, and it is crucial to extract personalized teaching techniques based on students' knowledge level. Cognitive Computing systems in the learning domain open a substantial range of possible alternatives, thereby helping both students and teachers. These systems enhance the human cognitive capabilities by developing cognitive assistant, which is a flexible and adaptive learning software and a continuous learning system. In this paper, a personalized cognitive layered framework is proposed to map the student's reasoning level with the mathematical teaching techniques. The analysis is done with real time data collected from the School for Grades 1 to 6 and mathematical teaching techniques extracted from mathematical research articles. The Hypothesis Evidence Extraction (HypoEE) algorithm is proposed to extract optimal teaching techniques. The proposed system follows the pipeline of cognitive computing processes such as extraction of factors of importance, knowledge assertion graph construction, node prioritization, correlation extraction, text summarization, hypothesis identification, evidence extraction and scoring, hypothesis refinement, inference and decision formulation. The system is evaluated using performance metrics and cognitive metrics. The validation is done by three mathematical subject experts. The results showcase a Confidence Weighted Score (CWS) of 0.96, an accuracy of 0.994, 0.92 precision, 0.88 recall, 0.89 F1 score, 0.83 specificity and cognitive metric scores indicating a taskability of 0.89, 0.86 knowledge capacity and 0.89 knowledge utilization. [Display omitted] • A personalized cognitive layered framework for deriving the most effective and personalized mathematical teaching strategy. • A cognitive system driven by evidence-based inference to personalize education. • A text summarization algorithm crafted to produce concise summaries from large volumes of text. • Application of the Hypothesis Evidence Extraction (HypoEE) algorithm to extract optimal mathematical teaching techniques based on individual reasoning levels. • Analyzing performance through expert assessments and cognitive evaluation metrics. [ABSTRACT FROM AUTHOR]

Text summarization is a widely-researched problem among scholars in the field of natural language processing. Multiple techniques have been proposed to help tackle this problem, yet some of these methodologies may still exhibit limitations such as the requirements for large training datasets, which might not always be possible, but more importantly, the lack of interpretability or transparency of the model. In this paper, we propose using meta-learning algorithm to train a deep learning model for extractive text summarization and then using various explanatory techniques such as SHAP (Shapley, 1953), linear regression (Lederer, 2022), decision trees (Fürnkranz, 2010), and input modification to gain insights into the model's decision making process. To evaluate the effectiveness of our approach, we will compare it to other popular natural language processing models like BERT (Miller, 2019) or XLNET (Yang et al., 2020) using the ROUGE metrics (Lin, 2004). Overall, our proposed approach provides a promising solution to the limitations of existing methods and a framework for improving the explainability of deep learning models in other natural language processing tasks. • Bi-LSTM and Meta-learning produce better results compared to other popular models. • Meta-learning can be utilized to improve performance in data-scarce scenarios. • Use SHAP, regression, decision tree, and text mutation to enhance interpretability. [ABSTRACT FROM AUTHOR]

• Text summarization in scientific research is crucial but time consuming and labor intensive, and it requires specific expertise; here, NLP is playing a role. • ChatGPT's drug information summarization was evaluated against human experts using >14 000 records from the FDA Labeling document for the first time. • ChatGPT reliably generates summaries closely resembling those of human experts, particularly in the context of summarizing drug safety information from labeling documents. • ChatGPT and its derivatives are valuable in drug discovery, improving drug labeling access and supporting FDA review processes. Text summarization is crucial in scientific research, drug discovery and development, regulatory review, and more. This task demands domain expertise, language proficiency, semantic prowess, and conceptual skill. The recent advent of large language models (LLMs), such as ChatGPT, offers unprecedented opportunities to automate this process. We compared ChatGPT-generated summaries with those produced by human experts using FDA drug labeling documents. The labeling contains summaries of key labeling sections, making them an ideal human benchmark to evaluate ChatGPT's summarization capabilities. Analyzing >14 000 summaries, we observed that ChatGPT-generated summaries closely resembled those generated by human experts. Importantly, ChatGPT exhibited even greater similarity when summarizing drug safety information. These findings highlight ChatGPT's potential to accelerate work in critical areas, including drug safety. [ABSTRACT FROM AUTHOR]

With the advent of transformer-based architectures, the contextual representation of text data has leveraged the query and the document to be represented in low-dimensional dense vector space. These vectors are learned embeddings of fixed sizes, resulting in deeper text understanding. In this study, we designed a pipeline for effectively retrieving documents from a large search space by combining the deeper text understanding capabilities of the transformer-based BERT model and a phrase embedding-based query expansion model. To learn the contextual representations, we fine-tuned a deep semantic matching model by separately encoding the document and the query. The encoder model is based on the Sentence BERT (SBERT) architecture, which separately generates dense vector representations of documents and queries. The study has also addressed the maximum token length limitation of transformer-based models through the summarization of lengthy documents. In addition, to improve the clarity and completeness of short queries and reduce the semantic gap, a phrase embedding-based query expansion model is employed. The documents and their dense vectors are indexed using the Elasticsearch engine, and matched them with query vectors for retrieving query-specific documents. Finally, the BERT-based cross-encoder model is used to re-rank the relevant records for each query. It performs full self-attention over the inputs, and yields richer text interactions to produce the final results. To assess performance, experiments are conducted on two well-known datasets, TREC-CDS-2014 and OHSUMED. A comparative analysis is carried out, which clearly demonstrates that the proposed framework produced competitive retrieval results. • Transformer-based contextual text representation approach is proposed for IR. • Bi-Encoder architecture is applied to learn the representations of documents & query. • BERT-based summarization is applied to address the limitation on sequence length. • Phrase Embedding-based QE technique is employed to expand short and unclear queries. • Re-ranking of the retrieval results is performed using the cross encoder model. [ABSTRACT FROM AUTHOR]

The Transformer-based summarization models rely solely on the attention mechanism for document encoding, making it difficult to accurately capture long-range dependencies in long documents due to the presence of attention redundancy. To address this issue, we propose an extractive summarization framework guided by a topic model (TopicSum) that utilizes a heterogeneous graph neural network to leverage the topic information as document-level features during the sentence selection process, thereby capturing the long-range dependencies among sentences. The sentence-level features in this topic model align with the basic unit of the extractive summarization task. Additionally, a memory mechanism is employed to dynamically store and update the memory module, reducing the potential of repetitive information guiding sentence selection. We evaluated the model on three large document datasets, namely Pubmed, arXiv, and GovReport, and achieved significantly higher Rouge scores than previous works, including extractive and abstractive models. Furthermore, our experiments demonstrate that recent highly regarded large language models such as ChatGPT are insufficient to handle the long document summarization task directly. The proposed approach in this paper exhibits sufficient competitiveness in terms of both generation quality and deployment conditions. [ABSTRACT FROM AUTHOR]

Abstractive dialogue summarization requires distilling and rephrasing key information from noisy multi-speaker documents. Combining pre-trained language models with input augmentation techniques has recently led to significant research progress. However, existing solutions still struggle to select relevant chat segments, primarily relying on open-domain and unsupervised annotators not tailored to the actual needs of the summarization task. In this paper, we propose DearWatson , a task-aware utterance-level annotation framework for improving the effectiveness and interpretability of pre-trained dialogue summarization models. Precisely, we learn relevant utterances in the source document and mark them with special tags, that then act as supporting evidence for the generated summary. Quantitative experiments are conducted on two datasets made up of real-life messenger conversations. The results show that DearWatson allows model attention to focus on salient tokens, achieving new state-of-the-art results in three evaluation metrics, including semantic and factuality measures. Human evaluation proves the superiority of our solution in semantic consistency and recall. Finally, extensive ablation studies confirm each module's importance, also exploring different annotation strategies and parameter-efficient fine-tuning of large generative language models. [ABSTRACT FROM AUTHOR]

Automatic text summarization is to obtain a summary by compressing the text while retaining its important information. Then users can obtain the important content of the text by reading the summary. In the research literatures, the extraction summary method is widely used and is also one type of the main research methods of summary methods. However, this extraction summary method still has some problems. The selection of the initial cluster center has not been carefully determined, and the sentence redundancy summarized is high in articles with complex sentences. In order to solve the above problems, this paper proposes an automatic text summarization method based on improved TextRank algorithm and K-Means clustering. This method combines the improved BM25 model and the TextRank algorithm to calculate the BM25 similarity between sentences and obtain the TR scores of sentences. The TR scores are used to select the initial center of clustering based on similarity difference judgment and maximum judgment. The final summary is obtained by combining the cluster scores and sentence scores. The experimental results show that the proposed method in this paper has better evaluation indicators containing ROUGE-1, ROUGE-2 and ROUGE-L than other comparison algorithms including Lead-3, TextRank and MBM25EMB on the DUC2004 dataset. In conclusion, the proposed method in this paper improves the accuracy of automatic text summarization and reduce the redundancy from documents. [ABSTRACT FROM AUTHOR]

Development of effective automatic summarization approaches for legal documents suffer from several challenges like extremely long document-summary pairs, lack of large scale training datasets with tractable document-summary token lengths, etc. In this work, we deal with the problem of legal document summarization by building a modified abstractive dataset from the original dataset. This ensures that the length of each document-summary pair is manageable and can be processed by the state of the art summarization approaches (such as BART). Secondly, we deal with the data scarcity problem by creating more number of training samples, from each of the original document-summary pair. This is done by creating multiple extractive summaries from each sample in the original dataset, following which ground-truth summary sentences are assigned to each of the extractive summary to generate new training samples. This results in a larger training dataset that can be utilized for fine-tuning summarization models. Our proposed approach has been evaluated on two different legal datasets- BillSum and Forum of Information Retrieval Evaluation (FIRE). With respect to the ROUGE metrics, the proposed approach is able to outperform pre-trained BART model fine-tuned on original dataset by (3 − 8) % for FIRE test sets, and by (1 − 3) % for the BillSum test sets. Considering the BERTScore metrics, the proposed approach obtains (1 − 2) % improvements on the FIRE test sets, while for the BillSum test sets (3 − 8) % improvements are observed. Such improvements suggest that the proposed dataset building approach can help achieve improved abstractive summarization of lengthy legal documents. • Lengthy nature and data scarcity are the two main challenges with legal documents. • Lengthy legal document summarization is handled by the proposed approach. • The data scarcity problem is handled by creating a feasible abstractive dataset. • A novel Extract-Then-Assign (ETA) approach is proposed. • ETA approach can greatly improve abstractive summarization of legal documents. [ABSTRACT FROM AUTHOR]

Automatically generating a survey draft is a challenge to text summarization research because it needs to select important sentences from important references in a large set of candidate papers for composing sections that are in line with section titles and different sections discuss the most relevant reference papers of different number, which are beyond the capability of previous text summarization approaches as they assume that all candidate papers should be included into one summary. This paper proposes an approach to generating survey draft according to a pattern consisting of sections with titles given by the user who requests the survey. The problem of generating each section can be divided into the following sub-problems: (1) rank the input scientific documents (in short documents) according to the title of a section, (2) determine the number of documents that are most relevant to the title, and (3) rank and select sentences from the selected documents according to the title. A position closeness distance of key word is proposed to rank a set of documents by measuring how closely two key words within section title are distributed within each document, which is used to rank the documents. The rationale is that the positions of the neighboring key words of a section title should be closer in more relevant documents than other words. As different sections have different number of selected documents, a method is proposed to determine the number of documents to be included into the current section based on the slope shape of the sorted rank curve of documents according to the section title. Based on the duality property of the closeness, ranks of sentences within a document can be directly obtained when the document is ranked according to the title of section, and both the importance and coherence of selected sentences can be reflected without extra calculation for ranking sentences. Experiments and manual evaluation show that the proposed methods achieve significant improvements compared with other approaches. The proposed approach is significant in applications as different surveys can be generated according to different patterns given by different users. [ABSTRACT FROM AUTHOR]

In Natural Language Processing (NLP) domain, the majority of automatic text summarization approaches depend on a prior knowledge of the language and/or the domain of the text being summarized. Such approaches requires language dependent part-of-speech taggers, parsers, databases, pre-structured lexicons, etc. In this research, we propose a novel automatic text summarization model, Text Documents - Language Agnostic Summarization Model (TxLASM), which is able to perform extractive text summarization task in language/domain agnostic manner. TxLASM depends on specific characteristics of the major elements of the text being summarized rather than its domain, context, or language and thus rules out the need for language dependent pre-processing tools, taggers, parsers, lexicons or databases. Within TxLASM , we present a novel technique for encoding the shapes of major text elements (paragraphs, sentences, n-grams and words); moreover, we present language independent preprocessing algorithms to normalize words and perform relative stemming or lemmatization. Those algorithms and its Shape-Coding technique enable the TxLASM to extract intrinsic features of text elements and score them statistically, and subsequently extract a representative summary that is independent of the text language, domain and context. TxLASM was applied on an English and Portuguese benchmark datasets, and the results were compared to twelve state-of-the-art approaches presented in recent literature. In addition, the model was applied on French and Spanish news datasets, and the results were compared to those obtained by standard commercial summarization tools. TxLASM has outperformed all the SOTA approaches as well as the commercial tools in all four languages while maintaining its language and domain agnostic nature. [ABSTRACT FROM AUTHOR]

With the development of pre-trained language models and large-scale datasets, automatic text summarization has attracted much attention from the community of natural language processing, but the progress of automatic summarization evaluation has stagnated. Although there have been efforts to improve automatic summarization evaluation, ROUGE has remained one of the most popular metrics for nearly 20 years due to its competitive evaluation performance. However, ROUGE is not perfect, there are studies have shown that it is suffering from inaccurate evaluation of abstractive summarization and limited diversity of generated summaries, both caused by lexical bias. To avoid the bias of lexical similarity, more and more meaningful embedding-based metrics have been proposed to evaluate summaries by measuring semantic similarity. Due to the challenge of accurately measuring semantic similarity, none of them can fully replace ROUGE as the default automatic evaluation toolkit for text summarization. To address the aforementioned problems, we propose a compromise evaluation framework (ROUGE-SEM) for improving ROUGE with semantic information, which compensates for the lack of semantic awareness through a semantic similarity module. According to the differences in semantic similarity and lexical similarity, summaries are classified into four categories for the first time, including good-summary, pearl-summary, glass-summary, and bad-summary. In particular, the back-translation technique is adopted to rewrite pearl-summary and glass-summary that are inaccurately evaluated by ROUGE to alleviate lexical bias. Through this pipeline framework, summaries are first classified by candidate summary classifier, then rewritten by categorized summary rewriter, and finally scored by rewritten summary scorer, which are efficiently evaluated in a manner consistent with human behavior. When measured using Pearson, Spearman, and Kendall rank coefficients, our proposal achieves comparable or higher correlations with human judgments than several state-of-the-art automatic summarization evaluation metrics in dimensions of coherence, consistency, fluency, and relevance. This also suggests that improving ROUGE with semantics is a promising direction for automatic summarization evaluation. [ABSTRACT FROM AUTHOR]

In this paper, we focus on analyzing the relationship between the input of source text and source image, and then through the integration and generalization of the multi-modal information (e.g., texts and images), outputs the multi-modal summarization including text and image. However, existing multi-modal summarization methods face several challenges: (1) Different modalities exist in different semantic spaces, and expressing the multi-modal information of source modalities in a similar semantic representation space is crucial. (2) For the result of text summarization, it is crucial to explore how to capture both the differences and similarities within the source modalities, which can reduce redundancy to improve the quality of text summarization. In order to overcome the challenge above, Multi-Modal Anchor Adaptation Learning for Multi-Modal Summarization (MA-Sum) has been proposed. Specifically, MA-Sum employs a novel and highly efficient image anchor selection method, which selects the object sample containing the richest image information as the image anchor. Simultaneously, it carefully selects the text sentence closely intertwined with the image semantics to serve as the language anchor. Therefore, the multi-modal anchor can be seen as a bridge for multi-modal alignment to alleviate the semantic gap between textual and visual. Moreover, based on the distance between the anchors and the semantic information in the respective modal, the positive and negative semantic information of each modal will be distinguished. Based on negative semantic information, the counterfactual learning mechanism is constructed to optimize the result of multi-modal summarization. Finally, the process of multi-modal features interaction is optimized by image summary which is chosen by using multi-modal anchors. According to the experimental results, compared with the state-of-art multi-modal summarization, our proposed MA-Sum can be optimized in terms of summarization consistency and completeness, so as to obtain the optimal multi-modal summarization metric. [ABSTRACT FROM AUTHOR]

• The select-then-extract framework can better alleviate class imbalance. • Jointing the Extractive Summarization task contributes to the ECPE task. • Multi-granularity cooperative graph can better capture inter-clause relations. • The global keyword helps highlight causal cues implied in distant clauses. • Integrated inference predictor improves the efficiency of reasoning. The class imbalance problem arises in the current end-to-end models for the emotion-cause pair extraction (ECPE) task, and a large number of negative clause pairs interfere model to depict the characteristics of emotion and cause. Most existing methods adopt a window-constrained strategy to limit the relative distance between candidate clauses to a smaller range to filter negative pairs. However, those methods ignore the semantic association between distant clauses, and using an artificial window size is imperfect due to the inherent ambiguity and delicacy of emotion. Inspired by the centrality principle of discourse, in this paper we propose a select-then-extract framework for the ECPE task, where the core clauses are first selected from the original document as candidate emotion and cause clauses, and then emotions, causes and emotion-cause pairs are jointly extracted from the candidate clauses. Specifically, our model includes two main components: core clause selector and emotion-cause pairs extractor. For the Core Clause Selector , we introduce the extractive document summarization (EDS) task and present a multi-granularity semantic awareness cooperative graph model (MGCOG) to extract core clauses from documents. Compared to the previous methods, the core clause selector is more effective for alleviating the category imbalance because the global causal cues and context can be captured by learning global keywords and inter-clause relationships, which determines more efficient candidate clauses. For Emotion-Cause Pairs Extractor , we put forward a multi-task learning model to jointly extract emotions, causes and emotion-cause pairs from the selected core clauses. Here, a multi-head attention is used to further model the relationship between candidate clauses, and a co-predictor is designed for assigning scores to all possible emotion-cause pairs. We further investigate the pipeline and the joint model under the select-then-extract framework, and show that the experimental results on benchmark datasets are consistently superior to the comparative baseline models. Extensive ablation experiments also verify the effectiveness of each component. [ABSTRACT FROM AUTHOR]

Although pretrained language models achieve high performance on various natural language processing tasks, they still require further improvements in the sentence embedding task. Many studies have improved performance in this task using pre-trained language models and contrastive learning, but these approaches are limited because they are based on naive average pooling and CLS tokens. Therefore, we propose an advanced sentence-embedding method based on weighted pooling that considers token importance. Specifically, the token importance is calculated by combining an explainable artificial-intelligence module with a text summarization model, and the final sentence embedding is derived through weighted pooling token embedding and token importance. Thus, we derive a sentence embedding that considers both the local information of the token embedding and the global information of the entire sentence. Experimental results reveal that our proposed sentence embedding outperforms other models on both text similarity tasks and text classification. Moreover, the proposed method's robustness is verified through the results of an ablation study. [ABSTRACT FROM AUTHOR]

• Mapping large-scale, and high spatial resolution ecological quality (EQ) by GEE. • Overall change of EQ in Central Asia is slight inverted U-shaped. • Driving paths for EQ: urbanization, agriculture, resource, climate and policy. • Land use intensity and agricultural water use efficiency are main factors for EQ. • Interaction between human and natural factors are increasingly important for EQ. With the frequent occurrence of worldwide extreme climate events, human-induced ecosystem degradation has seriously threatened the realization of the Sustainable Development Goals (SDGs), especially in arid ecologically fragile areas. Macro-scale ecological quality (EQ) monitoring and exploration of its driving mechanisms have become research hotspots. However, this research field still lacks a method framework with strong comparability, adaptability and transferability, which significantly restricts the applicability of research results. In this study, a method framework for exploring spatial and temporal changes in EQ and their driving mechanisms based on text summarization and information extraction is constructed. Taking Central Asia as a typical case, this study outlines its EQ driving paths, explores the influencing mechanisms of representative drivers, and verifies the effectiveness of this method framework based on the comparison of the spatial and temporal evolution of EQ at multiple scales. The results indicate that the overall EQ in Central Asia exhibited a slight inverted U-shaped trend, with its driving paths falling into five categories: urban expansion, agricultural development, resource extraction, climate change, and ecological protection. The fragmentation of areas with high EQ is the main landscape characteristic in Central Asia. Furthermore, land use intensity and agricultural water use efficiency are significant factors in Central Asia's EQ evolution. Over time, the interaction between anthropogenic and natural factors has played an essential role in EQ evolution in Central Asia, with interactions between altitude, climate aridity, agricultural water use efficiency, and land use intensity gradually intensifying. This study has an implication for the construction of method framework for EQ-related study in ecologically fragile areas at the macro scale. [ABSTRACT FROM AUTHOR]

Recently, large language models have shown great success on various abstractive summarization datasets. These datasets consist of numerous data that are enough to train a large number of parameters. However, for a new domain, there is a lack of labeled data to train those parameters and the model is easily overfitted to a small amount of data. In addition, because annotating document-summary pairs is too expensive and transfer learning using high-resource datasets causes a domain shifting problem, a low-resource abstractive summarization task is becoming necessary. Herein, we propose an efficient framework for low-resource abstractive summarization using a pointer-generator network and a meta-learning technique to address the above problems. Meta-learning using existing high-resource datasets enables our model to rapidly adapt to a new domain using limited data to solve the domain shifting problem. In addition, we explore the copy mechanism using a pointer-generator network that can copy words from a source document when generating a summary. The experimental results on 11 different datasets show that the proposed model outperforms the previous state-of-the-art models in low-resource abstractive summarization on most of the datasets. [ABSTRACT FROM AUTHOR]

A long-running event represents a continuous stream of information on a given topic, such as natural disasters, stock market updates, or even ongoing customer relationship. These news stories include hundreds of individual, time-dependent texts. Simultaneously, new technologies have profoundly transformed the way we consume information. The need to obtain quick, relevant, and digest updates continuously has become a crucial issue and creates new challenges for the task of automatic document summarization. To that end, we introduce an innovative unsupervised method based on two competing sequence-to-sequence models to produce short updated summaries. The proposed architecture relies on several parameters to balance the outputs from the two autoencoders. This relation enables the overall model to correlate generated summaries with relevant information coming from both current and previous news iterations. Depending on the model configuration, we are then able to control the novelty or the consistency of terms included in generated summaries. We evaluate our method on a modified version of the TREC 2013, 2014, and 2015 datasets to track continuous events from a single source. We not only achieve state-of-the-art performance similar to other more complex unsupervised sentence compression approaches, but also influence the information included in the model in the summaries. • Incremental summarization of real-time news events. • Unsupervised autoencoder for summarization of short texts related to the same event. • Semi-extractive method of accounting for information updates in the sentence compression task. • Promoting novelty or cohesion of information in incremental and update summarization processes. [ABSTRACT FROM AUTHOR]

The primary challenge faced in extractive text summarization is related to the scoring of sentences, with the critical factor for scoring being the manner in which the sentence representation is conducted. This study aims to investigate this hypothesis and to perform a detailed analysis of the impact of sentence representation techniques that have been used both semantically and syntactically. The study initially evaluated the empirical impact of individual syntactic and semantic features on the accuracy of summarization. To examine syntactic usage, a comprehensive list of 40 syntactic features was developed, while semantic representation was accomplished using sentence embeddings. Subsequently, an improved feature set was proposed that jointly utilizes syntactic and semantic features. To assess the impact of this feature set on the resulting summaries, the proposed sentence representation was tested on three distinct summarization corpora consisting of lengthy scientific documents across diverse domains. The assessment of summary evaluation and classification performance evaluation metrics was conducted to evaluate the quality of the resulting summaries. The findings of the experiments indicated that the summaries generated by the proposed feature set performed better than not only those obtained using individual features but even summaries produced by state-of-the-art methods. • In literature, text summarization has been handled by syntactic or semantic approach. • A new enhanced sentence representation was proposed to score sentences. • The sentence scoring model was a random forest fed by this enhanced representation. • The proposed model remarkably outperformed the baseline and state-of-the-art models. [ABSTRACT FROM AUTHOR]

Generative transformer-based models have achieved state-of-the-art performance in text summarization. Nevertheless, they still struggle in real-world scenarios with long documents when trained in low-resource settings of a few dozen labeled training instances, namely in low-resource summarization (LRS). This paper bridges the gap by addressing two key research challenges when summarizing long documents, i.e., long-input processing and document representation, in one coherent model trained for LRS. Specifically, our novel align-then-abstract representation learning model (Athena) jointly trains a segmenter and a summarizer by maximizing the alignment between the chunk-target pairs in output from the text segmentation. Extensive experiments reveal that Athena outperforms the current state-of-the-art approaches in LRS on multiple long document summarization datasets from different domains. [ABSTRACT FROM AUTHOR]

Sentiment classification aims to predict the sentiment label for a given text. Recently, several research efforts have been devoted to incorporate matching clues between text words and class labels into the learning process of text representation. However, these methods heavily rely on the availability of label content. Moreover, they simply capture the label-specific signals to measure each word's contribution by either implicitly employing a learnable label representation or explicitly leveraging the interaction between text words and labels via the interaction mechanism. To deal with these issues, in this paper, we propose a novel framework called Label-Guided Dual-view Sentiment Classifier (LGDSC). We first introduce a new strategy for generating an effective label description and then design a novel Dual-Channel Label-guided Attention Network (DLAN) to learn a text representation via two different channels. DLAN will be further leveraged to learn label-guided text representations from two different views. Extensive experimental results on four real-world datasets demonstrate that LGDSC consistently outperforms the state-of-the-art baseline methods. • Propose an inverse label entropy based strategy for generating effective label descriptions. • Design a dual-channel label-guided attention network to learn text representation via two different channels. • Extensive experiments conducted on four widely used datasets demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Automatic text summarization plays a vital role in text retrieval. However, very little interest, as well as attention to Hindi text summarization and the wide use of hybridization of an evolutionary and swarm-based method for solving optimization problems, motivates us to develop a technique for achieving better results. We have used a combination of the Title feature, Sentence length, Sentence position, Numerical Data, Thematic word, Proper Noun and Term frequency & Inverse Sentence Frequency to find the results. The proposed BPSO-IGA comprising Binary Particle Swarm Optimization (BPSO) and an improved genetic algorithm finds a well-formatted summary from multiple documents. The method for calculating the score of the sentence is proposed initially. A combination of BPSO and IGA finds the features' optimal scores and then is used to generate the final summary. The proposed method is compared with six well-known techniques, SummaRuNNer, ChunkRank, TGraph, UniRank, FF-SE, and MDS-ACS, on FIRE 2011 Hindi dataset. To strengthen the hypothesis that all seven features are required in the summary generation, the summary is also generated using only ONE and a combination of more than one feature. It is found that the best summary is generated when all seven features are considered. The BPSO-IGA performs best among all the methods for precision, recall, f-measure, cohesion, and non-redundancy. This research aims to design a multi-document extractive text summarizer for Hindi documents using a swarm intelligence and evolutionary algorithm-based hybrid approach. [ABSTRACT FROM AUTHOR]

In this paper, we propose DeepSumm, a novel method based on topic modeling and word embeddings for the extractive summarization of single documents. Recent summarization methods based on sequence networks fail to capture the long range semantics of the document which are encapsulated in the topic vectors of the document. In DeepSumm, our aim is to utilize the latent information in the document estimated via topic vectors and sequence networks to improve the quality and accuracy of the summarized text. Each sentence is encoded through two different recurrent neural networks based on probabilistic topic distributions and word embeddings, and then a sequence to sequence network is applied to each sentence encoding. The outputs of the encoder and the decoder in the sequence to sequence networks are combined after weighting using an attention mechanism and converted into a score through a multi-layer perceptron network. We refer to the score obtained through the topic model as Sentence Topic Score (STS) and to the score generated through word embeddings as Sentence Content Score (SCS). In addition, we propose Sentence Novelty Score (SNS) and Sentence Position Score (SPS) and perform a weighted fusion of the four scores for each sentence in the document to compute a Final Sentence Score (FSS). The proposed DeepSumm framework was evaluated on the standard DUC 2002 benchmark and CNN/DailyMail datasets. Experimentally, it was demonstrated that our method captures both the global and the local semantic information of the document and essentially outperforms existing state-of-the-art approaches for extractive text summarization with ROUGE-1, ROUGE-2, and ROUGE-L scores of 53.2, 28.7 and 49.2 on DUC 2002 and 43.3, 19.0 and 38.9 on CNN/DailyMail dataset. • DeepSumm for extractive summarization based on seq2seq networks. • Sentence encoded with probabilistic topic distributions and word embeddings RNNs. • Proposed Sentence Novelty Score, based on sentence content and topic embeddings. • Seq2seq attention networks extract content and topic Scores. • Summary generated using four sentence scores: Content, Topic, Novelty and Position. [ABSTRACT FROM AUTHOR]

• Automatic and accurate evaluation of summary is a key problem of text summarization research. • A framework for evaluating summary from fidelity, conciseness, and coherence. • An evaluation method based on the semantic link network representation of text. • The semantic link network plays an important role in representing and analyzing texts. Automatic evaluation of summary is one of the basic problems of automatic text summarization research. The commonly used approaches evaluate the informativeness, conciseness and coherence of summary separately based on different representations with weak semantics such as n-gram and vector of words. By transforming text into semantic link network, this paper proposes a general framework for automatically evaluating summary from the following aspects: (1) fidelity , the extent of a summary that conveys the core of its source text; (2) conciseness , the extent of non-redundancy within summary and, (3) coherence , the extent of relatedness between representations within summary, which are based on a uniform semantic representation of text. Experiments on the evaluation of the summarization of scientific papers and news show that the proposed framework achieves comparable or higher correlations with human judgments than the popular evaluation models. This research also verifies the role of the semantic link network in representing and analysing texts. [ABSTRACT FROM AUTHOR]

In this paper, we propose Ranksum, an approach for extractive text summarization of single documents based on the rank fusion of four multi-dimensional sentence features extracted for each sentence: topic information, semantic content, significant keywords, and position. The Ranksum obtains the sentence saliency rankings corresponding to each feature in an unsupervised way followed by the weighted fusion of the four scores to rank the sentences according to their significance. The scores are generated in completely unsupervised way, and a labeled document set is required to learn the fusion weights. Since we found that the fusion weights can generalize to other datasets, we consider the Ranksum as an unsupervised approach. To determine topic rank, we employ probabilistic topic models whereas semantic information is captured using sentence embeddings. To derive rankings using sentence embeddings, we utilize Siamese networks to produce abstractive sentence representation and then we formulate a novel strategy to arrange them in their order of importance. A graph-based strategy is applied to find the significant keywords and related sentence rankings in the document. We also formulate a sentence novelty measure based on bigrams, trigrams, and sentence embeddings to eliminate redundant sentences from the summary. The ranks of all the sentences – computed for each feature – are finally fused to get the final score for each sentence in the document. We evaluate our approach on publicly available summarization datasets — CNN/DailyMail and DUC 2002. Experimental results show that our approach outperforms other existing state-of-the-art summarization methods. [Display omitted] • A unified summarization framework with multi-dimensional sentence features. • Novel method to rank sentences using probabilistic topic vectors. • Sentences ranked with a new technique exploiting embeddings. • Novelty parameter utilizing bigrams, trigrams and sentence embeddings. • Rank-based fusion strategy for extractive summarization. [ABSTRACT FROM AUTHOR]

Automatic text summarization is a topic of great interest in many fields of knowledge. Particularly, query-oriented extractive multi-document text summarization methods have increased their importance recently, since they can automatically generate a summary according to a query given by the user. One way to address this problem is by multi-objective optimization approaches. In this paper, a memetic algorithm, specifically a Multi-Objective Shuffled Frog-Leaping Algorithm (MOSFLA) has been developed, implemented, and applied to solve the query-oriented extractive multi-document text summarization problem. Experiments have been conducted with datasets from Text Analysis Conference (TAC), and the obtained results have been evaluated with Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. The results have shown that the proposed approach has achieved important improvements with respect to the works of scientific literature. Specifically, 25.41%, 7.13%, and 30.22% of percentage improvements in ROUGE-1, ROUGE-2, and ROUGE-SU4 scores have been respectively reached. In addition, MOSFLA has been applied to medicine texts from the Topically Diverse Query Focus Summarization (TD-QFS) dataset as a case study. • The query-oriented text summarization problem is addressed. • The criteria of query relevance and redundancy reduction are optimized simultaneously. • A multi-objective memetic algorithm (MOSFLA) is designed and developed. • The experiments use the Text Analysis Conference datasets and the ROUGE metrics. • MOSFLA outperforms the results of other 12 approaches from the scientific literature. [ABSTRACT FROM AUTHOR]

Automatic Text Summarization (ATS) is an essential field in natural language processing that attempts to condense large text documents so that users can assimilate information quickly. It finds uses in medical document summarization, review generation, and opinion mining. This work investigated an unsupervised extractive summarization approach that combined clustering with topic modeling to reduce topic bias. Latent Dirichlet Allocation was used for topic modeling, while K-Medoids clustering was employed for summary generation. The approach was evaluated on three datasets—Wikihow, CNN/DailyMail, and the DUC2002 Corpus. The Recall Oriented-Understudy for Gisting Evaluation (ROUGE) metrics were used for comparative analysis against recently reported techniques, specifically ROUGE-1 (R-1), ROUGE-2 (R-2), and ROUGE-L (R-L). The suggested framework offered scores of 34.80%, 9.13%, and 32.30% on the Wikihow Dataset, 43.90%, 19.01%, and 41.50% on the CNN/DailyMail Dataset, and 49.35%, 31.53%, and 41.72% on the DUC2002 Corpus (R-1, R-2, R-L respectively). These reported metrics are found to be superior when compared to similar recent works. Further, execution time of the proposed method was also recorded and compared with counterparts, which established its superior speed. Based on these promising outcomes, it was concluded that an unsupervised extractive summarization approach with greater subtopic focus significantly improves over generic topic modeling semantic and deep learning approaches. [ABSTRACT FROM AUTHOR]

When summarizing an article, humans are habituated to fuse multiple related sentences to make the summary more concise and coherent. But most of the previous work focuses on the grammaticality of the fusion process and neglects the mechanism behind which sentences should be fused together. And there also lacks an effective training method for bridging the modules in the model to approach a global optimization. In this paper we propose FusionSum , a novel framework that imitates the behaviors of humans in summarization by explicitly modeling the sentence grouping and fusion process. It consists of an entity-aware sentence grouping module to identify salient sentences and combine them into groups, after which a unified sentence fusion module rewrites each group into a summary sentence. Furthermore, we also study the collaboration problem between these two modules and propose cooperative reinforcement learning method, which plays a maximax two-player game to approach global optimization. Automatic evaluation shows that our model significantly outperforms the strong baselines on three prevalent corpora. Human evaluation further demonstrates the summaries generated by our model are more concise and coherent. [ABSTRACT FROM AUTHOR]

Dialogue summarization aims to generate a summary that indicates the key points of a given dialogue. In this work, we propose an end-to-end neural model for dialogue summarization with two novel modules, namely, the supporting utterance flow modelling module and the fact regularization module. The supporting utterance flow modelling helps to generate a coherent summary by smoothly shifting the focus from the former utterances to the later ones. The fact regularization encourages the generated summary to be factually consistent with the ground-truth summary during model training, which helps to improve the factual correctness of the generated summary in inference time. Furthermore, we also introduce a new benchmark dataset for dialogue summarization. Extensive experiments on both existing and newly-introduced datasets demonstrate the effectiveness of our model. [ABSTRACT FROM AUTHOR]