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As the amount of the available information continues to grow, finding the relevant information has become increasingly challenging. As a solution, text summarization has emerged as a vital method for extracting essential information from lengthy documents. There are various techniques available for filtering documents and extracting the pertinent information. In this study, a comparative analysis is conducted to evaluate traditional approaches and state-of-the-art methods on the BBC News and CNN/DailyMail datasets. This study offers valuable insights for researchers to advance their research and helps practitioners in selecting the most suitable techniques for their specific use cases.

Abstract Background Text summarization is a challenging problem in Natural Language Processing, which involves condensing the content of textual documents without losing their overall meaning and information content, In the domain of bio-medical research, summaries are critical for efficient data analysis and information retrieval. While several bio-medical text summarizers exist in the literature, they often miss out on an essential text aspect: text semantics. Results This paper proposes a novel extractive summarizer that preserves text semantics by utilizing bio-semantic models. We evaluate our approach using ROUGE on a standard dataset and compare it with three state-of-the-art summarizers. Our results show that our approach outperforms existing summarizers. Conclusion The usage of semantics can improve summarizer performance and lead to better summaries. Our summarizer has the potential to aid in efficient data analysis and information retrieval in the field of biomedical research.

Natural language processing (NLP) performs a vital function in text summarization, a task targeted at refining the crucial information from the massive quantity of textual data. NLP methods allow computers to comprehend and process human language, permitting the development of advanced summarization methods. Text summarization includes the automatic generation of a concise and coherent summary of a specified document or collection of documents. Extracting significant insights from text data is crucial as it provides advanced solutions to end-users and business organizations. Automatic text summarization (ATS) computerizes text summarization by decreasing the initial size of the text without the loss of main data features. Deep learning (DL) approaches exhibited significant performance in abstractive and extractive summarization tasks. This research designed an extractive text summarization using NLP with an optimal DL (ETS-NLPODL) model. The major goal of the ETS-NLPODL technique was to exploit feature selection with a hyperparameter-tuned DL model for summarizing the text. In the ETS-NLPODL technique, an initial step of data preprocessing was involved to convert the input text into a compatible format. Next, a feature extraction process was carried out and the optimal set of features was chosen by the hunger games search optimization (HGSO) algorithm. For text summarization, the ETS-NLPODL model used an attention-based convolutional neural network with a gated recurrent unit (ACNN-GRU) model. Finally, the mountain gazelle optimization (MGO) algorithm was employed for the optimal hyperparameter selection of the ACNN-GRU model. The experimental results of the ETS-NLPODL system were examined under the benchmark dataset. The experimentation outcomes pointed out that the ETS-NLPODL technique gained better performance over other methods concerning diverse performance measures.

This study presents a pioneering approach that leverages advanced sensing technologies and data processing techniques to enhance the process of clinical documentation generation during medical consultations. By employing sophisticated sensors to capture and interpret various cues such as speech patterns, intonations, or pauses, the system aims to accurately perceive and understand patient–doctor interactions in real time. This sensing capability allows for the automation of transcription and summarization tasks, facilitating the creation of concise and informative clinical documents. Through the integration of automatic speech recognition sensors, spoken dialogue is seamlessly converted into text, enabling efficient data capture. Additionally, deep models such as Transformer models are utilized to extract and analyze crucial information from the dialogue, ensuring that the generated summaries encapsulate the essence of the consultations accurately. Despite encountering challenges during development, experimentation with these sensing technologies has yielded promising results. The system achieved a maximum ROUGE-1 metric score of 0.57, demonstrating its effectiveness in summarizing complex medical discussions. This sensor-based approach aims to alleviate the administrative burden on healthcare professionals by automating documentation tasks and safeguarding important patient information. Ultimately, by enhancing the efficiency and reliability of clinical documentation, this innovative method contributes to improving overall healthcare outcomes. [ABSTRACT FROM AUTHOR]

The automatic generation of natural language is a complex and essential task in text processing. This study proposes a novel approach to address this fundamental problem by leveraging an improved version of DST_BERT, a model that converts input text into a vector representation. Our key contribution lies in the joint optimization of two models, NLU (Natural Language Under-standing) and NLG (Natural Language Generation), which enables us to obtain variable representations within a hidden space. This integration enhances the capabilities of both NLU and NLG in generating coherent and contextually appropriate language. The NLU and NLG models are seamlessly integrated with the hidden variable space, forming a generative representation model. To assess the effectiveness of our proposed approach, we conducted extensive experiments on the E2E and Weather datasets. The results highlight the state-of-the-art performance achieved by our model in generating natural language. [ABSTRACT FROM AUTHOR]

The explosive growth of dialogue data has aroused significant interest among scholars in abstractive dialogue summarization. In this paper, we propose a novel sequence-to-sequence framework called DS-SS (Dialogue Summarization with Factual-Statement Fusion and Dialogue Segmentation) for summarizing dialogues. The novelty of the DS-SS framework mainly lies in two aspects: 1) Factual statements are extracted from the source dialogue and combined with the source dialogue to perform the further dialogue encoding; and 2) A dialogue segmenter is trained and used to separate a dialogue to be encoded into several topic-coherent segments. Thanks to these two aspects, the proposed framework may better encode dialogues, thereby generating summaries exhibiting higher factual consistency and informativeness. Experimental results on two large-scale datasets SAMSum and DialogSum demonstrate the superiority of our framework over strong baselines, as evidenced by both automatic evaluation metrics and human evaluation. [ABSTRACT FROM AUTHOR]

Due to the enormous content of text available online through emails, social media, and news articles, it has become complicated to summarize the textual information from multiple documents. Text summarization automatically creates a comprehensive description of the document that retains its informative contents through the keywords, where Multi-Document Summarization (MDS) is a productive tool for data accumulation that creates a concise and informative summary from the documents. In order to extract the relevant information from the documents, Graph neural networks (GNNs) is the neural structure that detains the interrelation of the graph by progressing the messages between the graphical nodes. In the current years, the advanced version of GNNs, such as graph attention network (GAN), graph recurrent network, and graph convolutional network (GCN) provides a remarkable performance in text summarization with the advantage of deep learning techniques. Hence, in this survey, graph approaches for text summarization has been analyzed and discussed, where the recent text summarization model based on Deep learning techniques are highlighted. Further, the article provides the taxonomy to abstract the design pattern of Neural Networks and conducts a comprehensive of the existing text summarization model. Finally, the review article enlists the future direction of the researcher, which would motivate the enthusiastic and novel contributions in text summarizations. [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]

In recent years, deep learning has revolutionized natural language processing (NLP) by enabling the development of models that can learn complex representations of language data, leading to significant improvements in performance across a wide range of NLP tasks. Deep learning models for NLP typically use large amounts of data to train deep neural networks, allowing them to learn the patterns and relationships in language data. This is in contrast to traditional NLP approaches, which rely on hand-engineered features and rules to perform NLP tasks. The ability of deep neural networks to learn hierarchical representations of language data, handle variable-length input sequences, and perform well on large datasets makes them well-suited for NLP applications. Driven by the exponential growth of textual data and the increasing demand for condensed, coherent, and informative summaries, text summarization has been a critical research area in the field of NLP. Applying deep learning to text summarization refers to the use of deep neural networks to perform text summarization tasks. In this survey, we begin with a review of fashionable text summarization tasks in recent years, including extractive, abstractive, multi-document, and so on. Next, we discuss most deep learning-based models and their experimental results on these tasks. The paper also covers datasets and data representation for summarization tasks. Finally, we delve into the opportunities and challenges associated with summarization tasks and their corresponding methodologies, aiming to inspire future research efforts to advance the field further. A goal of our survey is to explain how these methods differ in their requirements as understanding them is essential for choosing a technique suited for a specific setting. This survey aims to provide a comprehensive review of existing techniques, evaluation methodologies, and practical applications of automatic text summarization. [ABSTRACT FROM AUTHOR]

In this digital era, there is a tremendous increase in the volume of data, which adds difficulties to the person who utilizes particular applications, such as websites, email, and news. Text summarization targets to reduce the complexity of obtaining statistics from the websites as it compresses the textual document to a short summary without affecting the relevant information. The crucial step in multi-document summarization is obtaining a relationship between the cross-sentence. However, the conventional methods fail to determine the inter-sentence relationship, especially in long documents. This research develops a graph-based neural network to attain an inter-sentence relationship. The significant step in the proposed multi-document text summarization model is forming the weighted graph embedding features. Furthermore, the weighted graph embedding features are utilized to evaluate the relationship between the document's words and sentences. Finally, the bidirectional long short-term memory (BiLSTM) classifier is utilized to summarize the multi-document text summarization. The experimental analysis uses the three standard datasets, the Daily Mail dataset, Document Understanding Conference (DUC) 2002, and Document Understanding Conference (DUC) 2004 dataset. The experimental outcome demonstrates that the proposed weighted graph embedding feature + BiLSTM model exceeds all the conventional methods with Precision, Recall, and F1 score of 0.5352, 0.6296, and 0.5429, respectively. [ABSTRACT FROM AUTHOR]

Text summarizing (TS) takes key information from a source text and condenses it for the user while retaining the primary material. When it comes to text summaries, the most difficult problem is to provide broad topic coverage and diversity in a single summary. Overall, text summarization addresses the fundamental need to distill large volumes of information into more manageable and digestible forms, making it a crucial technology in the era of information abundance. It benefits individuals, businesses, researchers, and various other stakeholders by enhancing efficiency and comprehension in dealing with textual data. In this paper, proposed a novel Modified Generative adversarial network (MGAN) for summarize the text. The proposed model involves three stages namely pre-processing, Extractive summarization, and summary generation. In the first Phase, the Text similarity dataset is pre-processed using Lowering Casing, Tokenization, Lemmatization, and, Stop Word Removal. In the second Phase, the Extractive summarization is done in three steps Generating similarity metrics, Sentence Ranking, and Sentence Extractive. In the third stage, a generative adversarial network (GAN) employs summary generation to jointly train the discriminative model D and the generative model G. To classify texts and annotate their syntax, Generative Model G employs a convolutional neural network called Bidirectional Gated Recursive Unit (CNN-BiGRU). The performance analysis of the proposed MGAN is calculated based on the parameters like accuracy, specificity, Recall, and Precision metrics. The proposed MGAN achieves an accuracy range of 99%. The result shows that the proposed MGAN improves the overall accuracy better than 9%, 6.5% and 5.4% is DRM, LSTM, and CNN respectively. [ABSTRACT FROM AUTHOR]

Automatic text summarization is the task of creating concise and fluent summaries without human intervention while preserving the meaning of the original text document. To increase the readability of the languages, a summary should be generated. In this paper, a novel Nesterov-accelerated Adaptive Moment Estimation Optimization based on Long Short-Term Memory [NADAM-LSTM] has been proposed to summarize the text. The proposed NADAM-LSTM model involves three stages namely pre-processing, summary generation, and parameter tuning. Initially, the Giga word Corpus dataset is pre-processed using Tokenization, Word Removal, Stemming, Lemmatization, and Normalization for removing irrelevant data. In the summary generation phase, the text is converted to the word-to-vector method. Further, the text is fed to LSTM to summarize the text. The parameter of the LSTM is then tuned using NADAM Optimization. The performance analysis of the proposed NADAM-LSTM is calculated based on parameters like accuracy, specificity, Recall, Precision, and F1 score. The suggested NADAM-LSTM achieves an accuracy range of 99.5%. The result illustrates that the proposed NADAM-LSTM enhances the overall accuracy better than 12%, 2.5%, and 1.5% in BERT, CNN-LSTM, and RNN respectively. [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]

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]

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]

Abstractive summarization is an approach to document summarization that is not limited to selecting sentences from the document but can generate new sentences as well. We address the two main challenges in abstractive summarization: how to evaluate the performance of a summarization model and what is a good training objective. We first introduce new evaluation measures based on the semantic similarity of the input and corresponding summary. The similarity scores are obtained by the fine-tuned BERTurk model using either the cross-encoder or a bi-encoder architecture. The fine-tuning is done on the Turkish Natural Language Inference and Semantic Textual Similarity benchmark datasets. We show that these measures have better correlations with human evaluations compared to Recall-Oriented Understudy for Gisting Evaluation (ROUGE) scores and BERTScore. We then introduce a deep reinforcement learning algorithm that uses the proposed semantic similarity measures as rewards, together with a mixed training objective, in order to generate more natural summaries in terms of human readability. We show that training with a mixed training objective function compared to only the maximum-likelihood objective improves similarity scores. [ABSTRACT FROM AUTHOR]

Recently, neural abstractive text summarization (NATS) models based on sequence-to-sequence architecture have drawn a lot of attention. Real-world texts that need to be summarized range from short news with dozens of words to long reports with thousands of words. However, most existing NATS models are not good at summarizing long documents, due to the inherent limitations of their underlying neural architectures. In this paper, we focus on the task of long document summarization (LDS). Based on the inherent section structures of source documents, we divide an abstractive LDS problem into several smaller-sized problems. In this circumstance, how to provide a less-biased target summary as the supervision for each section is vital for the model's performance. As a preliminary, we formally describe the section-to-summary-sentence (S2SS) alignment for LDS. Based on this, we propose a novel NATS framework for the LDS task. Our framework is built based on the theory of unbalanced optimal transport (UOT), and it is named as UOTSumm. It jointly learns three targets in a unified training objective, including the optimal S2SS alignment, a section-level NATS summarizer, and the number of aligned summary sentences for each section. In this way, UOTSumm directly learns the text alignment from summarization data, without resorting to any biased tool such as ROUGE. UOTSumm can be easily adapted to most existing NATS models. And we implement two versions of UOTSumm, with and without the pretrain-finetune technique. We evaluate UOTSumm on three publicly available LDS benchmarks: PubMed , arXiv , and GovReport. UOTSumm obviously outperforms its counterparts that use ROUGE for the text alignment. When combined with UOTSumm, the performance of two vanilla NATS models improves by a large margin. Besides, UOTSumm achieves better or comparable performance when compared with some recent strong baselines. [ABSTRACT FROM AUTHOR]

The article discusses the use of Artificial Intelligence (A.I.) tools, specifically chatbots, in second language acquisition. It highlights the potential benefits and concerns associated with the use of A.I. in language learning. The article calls for systematic research to explore the capabilities of A.I. language models, such as ChatGPT, and their impact on language learning. It suggests three strands of research: examining the language model itself, investigating learner interactions with the model, and exploring the effects of the model on learners. The article emphasizes the need to connect this research with existing studies in Instructed Second Language Acquisition (ISLA) to enhance our understanding of language learning conditions. [Extracted from the article]

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]

Natural language processing (NLP) performs a vital function in text summarization, a task targeted at refining the crucial information from the massive quantity of textual data. NLP methods allow computers to comprehend and process human language, permitting the development of advanced summarization methods. Text summarization includes the automatic generation of a concise and coherent summary of a specified document or collection of documents. Extracting significant insights from text data is crucial as it provides advanced solutions to end-users and business organizations. Automatic text summarization (ATS) computerizes text summarization by decreasing the initial size of the text without the loss of main data features. Deep learning (DL) approaches exhibited significant performance in abstractive and extractive summarization tasks. This research designed an extractive text summarization using NLP with an optimal DL (ETS-NLPODL) model. The major goal of the ETS-NLPODL technique was to exploit feature selection with a hyperparameter-tuned DL model for summarizing the text. In the ETS-NLPODL technique, an initial step of data preprocessing was involved to convert the input text into a compatible format. Next, a feature extraction process was carried out and the optimal set of features was chosen by the hunger games search optimization (HGSO) algorithm. For text summarization, the ETS-NLPODL model used an attention-based convolutional neural network with a gated recurrent unit (ACNN-GRU) model. Finally, the mountain gazelle optimization (MGO) algorithm was employed for the optimal hyperparameter selection of the ACNNGRU model. The experimental results of the ETS-NLPODL system were examined under the benchmark dataset. The experimentation outcomes pointed out that the ETS-NLPODL technique gained better performance over other methods concerning diverse performance measures. [ABSTRACT FROM AUTHOR]

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Natural language processing (NLP) is a technology that has become widespread in the area of human language understanding and analysis. A range of text processing tasks such as summarisation, semantic analysis, classification, question-answering, and natural language inference are commonly performed using it. The dilemma of picking a model to help us in our task is still there. It's becoming an impediment. This is where we are trying to determine which modern NLP models are better suited for the tasks set out above in order to compare them with datasets like SQuAD and GLUE. For comparison, BERT, RoBERTa, distilBERT, BART, ALBERT, and text-to-text transfer transformer (T5) models have been used in this study. The aim is to understand the underlying architecture, its effects on the use case and also to understand where it falls short. Thus, we were able to observe that RoBERTa was more effective against the models ALBERT, distilBERT, and BERT in terms of tasks related to semantic analysis, natural language inference, and question-answering. The reason is due to the dynamic masking present in RoBERTa. For summarisation, even though BART and T5 models have very similar architecture the BART model has performed slightly better than the T5 model. [ABSTRACT FROM AUTHOR]

For over a decade, online job portals have been providing their services to both job seekers and employers in search of hiring opportunities. Because of the high demand for recruitment, it is insufficient to use conventional hiring methods to find a suitable candidate to fill the position. Validating resumes online is challenging due to the potential for manual errors, making the process inherently risky. The bidirectional method comprises named entity recognition (NER) for extracting the required resumes for recruiters. Cosine similarity shows the match percentage of resumes for the job requirements and vice versa. In an attempt to tackle an issue of unregistered words, a solution called decoder attention with pointer network (DA-PN) has been introduced. This method incorporates the use of coverage mechanism to prevent word repetition through generated text summary. DA-PN+Cover method with mixed learning objective (MLO) (DA-PN+Cover+MLO) is utilized for protecting grow of increasing faults in generated text summary. Performance of proposed method is estimated using evaluation indicator recall oriented understudy for gisting evaluation (ROUGE) and attains an average of 27.47 which is comparatively higher than existing methods. [ABSTRACT FROM AUTHOR]

Medical imaging description and disease diagnosis are vitally important yet time-consuming. Automated diagnosis report generation (DRG) from medical imaging description can reduce clinicians' workload and improve their routine efficiency. To address this natural language generation task, fine-tuning a pre-trained large language model (LLM) is cost-effective and indispensable, and its success has been witnessed in many downstream applications. However, semantic inconsistency of sentence embeddings has been massively observed from undesirable repetitions or unnaturalness in text generation. To address the underlying issue of anisotropic distribution of token representation, in this study, a contrastive learning penalized cross-entropy (CLpCE) objective function is implemented to enhance the semantic consistency and accuracy of token representation by guiding the fine-tuning procedure towards a specific task. Furthermore, to improve the diversity of token generation in text summarization and to prevent sampling from unreliable tail of token distributions, a diversity contrastive search (DCS) decoding method is designed for restricting the report generation derived from a probable candidate set with maintained semantic coherence. Furthermore, a novel metric named the maximum of token repetition ratio (maxTRR) is proposed to estimate the token diversity and to help determine the candidate output. Based on the LLM of a generative pre-trained Transformer 2 (GPT-2) of Chinese version, the proposed CLpCE with DCS (CLpCEwDCS) decoding framework is validated on 30,000 desensitized text samples from the "Medical Imaging Diagnosis Report Generation" track of 2023 Global Artificial Intelligence Technology Innovation Competition. Using four kinds of metrics evaluated from n-gram word matching, semantic relevance, and content similarity as well as the maxTRR metric extensive experiments reveal that the proposed framework effectively maintains semantic coherence and accuracy (BLEU-1, 0.4937; BLEU-2, 0.4107; BLEU-3, 0.3461; BLEU-4, 0.2933; METEOR, 0.2612; ROUGE, 0.5182; CIDER, 1.4339) and improves text generation diversity and naturalness (maxTRR, 0.12). The phenomenon of dull or repetitive text generation is common when fine-tuning pre-trained LLMs for natural language processing applications. This study might shed some light on relieving this issue by developing comprehensive strategies to enhance semantic coherence, accuracy and diversity of sentence embeddings. [ABSTRACT FROM AUTHOR]

In a multi-document summarization task, if the user can decide on the summary topic, the generated summary can better align with the reader's specific needs and preferences. This paper addresses the issue of overly general content generation by common multi-document summarization models and proposes a topic-oriented multi-document summarization (TOMDS) approach. The method is divided into two stages: extraction and abstraction. During the extractive stage, it primarily identifies and retrieves paragraphs relevant to the designated topic, subsequently sorting them based on their relevance to the topic and forming an initial subset of documents. In the abstractive stage, building upon the transformer architecture, the process includes two parts: encoding and decoding. In the encoding part, we integrated an external discourse parsing module that focuses on both micro-level within-paragraph semantic relationships and macro-level inter-paragraph connections, effectively combining these with the implicit relationships in the source document to produce more enriched semantic features. In the decoding part, we incorporated a topic-aware attention mechanism that dynamically zeroes in on information pertinent to the chosen topic, thus guiding the summary generation process more effectively. The proposed model was primarily evaluated using the standard text summary dataset WikiSum. The experimental results show that our model significantly enhanced the thematic relevance and flexibility of the summaries and improved the accuracy of grammatical and semantic comprehension in the generated summaries. [ABSTRACT FROM AUTHOR]

Text summarizing (TS) takes key information from a source text and condenses it for the user while retaining the primary material. When it comes to text summaries, the most difficult problem is to provide broad topic coverage and diversity in a single summary. Overall, text summarization addresses the fundamental need to distill large volumes of information into more manageable and digestible forms, making it a crucial technology in the era of information abundance. It benefits individuals, businesses, researchers, and various other stakeholders by enhancing efficiency and comprehension in dealing with textual data. In this paper, proposed a novel Modified Generative adversarial network (MGAN) for summarize the text. The proposed model involves three stages namely pre-processing, Extractive summarization, and summary generation. In the first Phase, the Text similarity dataset is pre-processed using Lowering Casing, Tokenization, Lemmatization, and, Stop Word Removal. In the second Phase, the Extractive summarization is done in three steps Generating similarity metrics, Sentence Ranking, and Sentence Extractive. In the third stage, a generative adversarial network (GAN) employs summary generation to jointly train the discriminative model D and the generative model G. To classify texts and annotate their syntax, Generative Model G employs a convolutional neural network called Bidirectional Gated Recursive Unit (CNN-BiGRU). The performance analysis of the proposed MGAN is calculated based on the parameters like accuracy, specificity, Recall, and Precision metrics. The proposed MGAN achieves an accuracy range of 99%. The result shows that the proposed MGAN improves the overall accuracy better than 9%, 6.5% and 5.4% is DRM, LSTM, and CNN respectively. [ABSTRACT FROM AUTHOR]

Automatic text summarization is the task of creating concise and fluent summaries without human intervention while preserving the meaning of the original text document. To increase the readability of the languages, a summary should be generated. In this paper, a novel Nesterov-accelerated Adaptive Moment Estimation Optimization based on Long Short-Term Memory [NADAM-LSTM] has been proposed to summarize the text. The proposed NADAM-LSTM model involves three stages namely pre-processing, summary generation, and parameter tuning. Initially, the Giga word Corpus dataset is pre-processed using Tokenization, Word Removal, Stemming, Lemmatization, and Normalization for removing irrelevant data. In the summary generation phase, the text is converted to the word-to-vector method. Further, the text is fed to LSTM to summarize the text. The parameter of the LSTM is then tuned using NADAM Optimization. The performance analysis of the proposed NADAM-LSTM is calculated based on parameters like accuracy, specificity, Recall, Precision, and F1 score. The suggested NADAM-LSTM achieves an accuracy range of 99.5%. The result illustrates that the proposed NADAM-LSTM enhances the overall accuracy better than 12%, 2.5%, and 1.5% in BERT, CNN-LSTM, and RNN respectively. [ABSTRACT FROM AUTHOR]

Online learning text summarization is vital for managing the constant influx of online information. It involves condensing lengthy online content into concise summaries while retaining the original meaning and information. While several online summarization tools are available, they often fall short in preserving the underlying semantics of the text. In this paper, we introduce an innovative approach to online text summarization that strongly emphasizes capturing and preserving the semantics of the text. Our automatic summarizer leverages distributional semantic models to extract and incorporate semantics, producing high-quality online summaries. To evaluate the effectiveness of our online summarization system, we conducted experiments on a diverse range of online content. We employed ROUGE metrics, a popular evaluation method for text summarization, to assess our system's performance. Additionally, we compared our results with those of four state-of-the-art online summarizers. The outcome of our study demonstrates that our online summarization approach, which integrates semantics as a fundamental feature, outperforms other reference summarizers. This conclusion underscores the significance of leveraging semantics in the context of online learning text summarization. Furthermore, our system's ability to reduce redundancies in online content makes it a valuable tool for managing information overload in the digital age. [ABSTRACT FROM AUTHOR]

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(Source) Code summarization aims to automatically generate summaries/comments for given code snippets in the form of natural language. Such summaries play a key role in helping developers understand and maintain source code. Existing code summarization techniques can be categorized into extractive methods and abstractive methods. The extractive methods extract a subset of important statements and keywords from the code snippet using retrieval techniques and generate a summary that preserves factual details in important statements and keywords. However, such a subset may miss identifier or entity naming, and consequently, the naturalness of the generated summary is usually poor. The abstractive methods can generate human-written-like summaries leveraging encoder-decoder models. However, the generated summaries often miss important factual details. To generate human-written-like summaries with preserved factual details, we propose a novel extractive-and-abstractive framework. The extractive module in the framework performs the task of extractive code summarization, which takes in the code snippet and predicts important statements containing key factual details. The abstractive module in the framework performs the task of abstractive code summarization, which takes in the code snippet and important statements in parallel and generates a succinct and human-written-like natural language summary. We evaluate the effectiveness of our technique, called EACS, by conducting extensive experiments on three datasets involving six programming languages. Experimental results show that EACS significantly outperforms state-of-the-art techniques for all three widely used metrics, including BLEU, METEOR, and ROUGH-L. In addition, the human evaluation demonstrates that the summaries generated by EACS have higher naturalness and informativeness and are more relevant to given code snippets. [ABSTRACT FROM AUTHOR]

Automated text summarization (ATS) systems rely on language resources as datasets. However, creating these datasets is a complex and labor-intensive task requiring linguists to extensively annotate the data. Consequently, certain public datasets for ATS, particularly in languages such as Thai, are not as readily available as those for the more popular languages. The primary objective of the ATS approach is to condense large volumes of text into shorter summaries, thereby reducing the time required to extract information from extensive textual data. Owing to the challenges involved in preparing language resources, publicly accessible datasets for Thai ATS are relatively scarce compared to those for widely used languages. The goal is to produce concise summaries and accelerate the information extraction process using vast amounts of textual input. This study introduced ThEconSum, an ATS architecture specifically designed for Thai language, using economy-related data. An evaluation of this research revealed the significant remaining tasks and limitations of the Thai language. [ABSTRACT FROM AUTHOR]

Text mining techniques are used for trend prediction using keyword analysis; however, these processes result in the formation of relevant and irrelevant keywords. Based on the keywords, various clusters are formed, resulting in various topics. Due to the presence of irrelevant keywords, there are chances of the formation of wrong topics. To overcome this problem, this research contributes to developing an algorithm that deals with topic prediction using a noble technique wherein text summarization is inculcated into topic modeling algorithms. This research focuses on implementing text summarizing to generate summaries of published publications by diverse researchers using the Genism library with an extractive text summarization approach and then applying text mining to it to predict trends in various fields. The current approach was compared with existing techniques based on the parameters used in automatic and semi-automatic text mining techniques. [ABSTRACT FROM AUTHOR]

The task of text summarization aims to provide highly condensed summaries of long textual information, with the ideal summary being both precise and concise. In recent years, there has been extensive research on the brevity of summaries, but these methods still have significant room for improvement in ROUGE scores, especially when the beam width is increased. We propose a new model called the DC (Dual-Level Contrastive Learning), which combines contrastive learning and data augmentation, and design a new scoring function during the training phase to enhance accuracy and conciseness. Ultimately, our framework achieves excellent ROUGE scores, ensuring concise and readable output even with increased beam width. Experimental results on the CNN/DailyMail (47.82 ROUGE-1, 0.017 VAR) and XSum (47.31 ROUGE-1, 0.0052 VAR) datasets demonstrate that our approach can significantly enhance the accuracy and conciseness of the summaries. Some metrics have exceeded those of the current state-of-the-art model BRIO, promoting the state-of-the-art performance to a higher level.

Urdu, being a common language in South Asia, has not received significant attention in terms of language processing compared to more advanced languages. In the field of Natural Language Processing (NLP), the task of text summarization holds great importance due to its ability to comprehend textual content and generate concise summaries. Text summarization can be either extractive or abstractive in nature. While considerable efforts have been made to advance extractive summarization techniques, the limitations associated with it have been extensively explored and explained in the paper. However, the domain of abstractive summarization for the Urdu language remains largely unexplored. The challenges and underlying factors that have impeded progress in this domain have also been addressed. This paper specifically focuses on abstractive summarization of the Urdu language using supervised learning. To accomplish this, a labeled dataset consisting of Urdu text and its abstractive summaries is required. A dataset of Urdu text and its corresponding abstractive summaries has been prepared for the purpose of supervised learning. Additionally, the paper presents the results of summary generation, measured in terms of a rough score. Transformer’s encoder-decoder network was employed to generate abstractive summaries in Urdu, yielding a ROUGE-1 score of 25.18 in Urdu text summarization. Moreover, a novel evaluation metric called the “disconnection rate” has been introduced as a context-aware evaluation metric to enhance the assessment of a summary, known as the Context Aware RoBERTa Score.

The paper describes automatic summarization as one of the topic that helps elearning system to be more adaptable on content generation. This article treat automatic summarization with approaches that provide the ability to summarize texts for different languages. In the case of this article, it is about the English, Romanian and Russian languages. The paper contains both the description of the problem and different approaches already used by other researchers. Next, the data with which the automatic summarization experiments were carried out were described. The metrics with which we can evaluate the quality of the summarization result were presented. Finally, some thoughts were formulated regarding the results obtained in the experiment.

Expert-caliber documents, reports, letters, and resumes can be easily developed using Microsoft Office. Microsoft Office offers capabilities such as grammar check, text and font checking & formatting, HTML compatibility, advanced page layout, image support, and more in contrast to a plain text editor, however, it does not have the autocomplete abbreviations feature. The paper proposes an Autocomplete abbreviation Recommendation System that will integrate the benefits of getting automatic suggestions of either full forms, abbreviations, or both by clicking on the option that is being suggested. This will provide more flexibility to the user using existing Microsoft Office platforms. To create this feature, we have examined the JavaScript JQuery functions to implement a basic autocomplete feature. Information overloading is also one of the most important problems brought on by the Internet's explosive expansion. Massive quantities of text are difficult for people to manually summarise. Thus, there is now a greater need for summarizers that are more sophisticated and potent. Hence, Python's packages, methods, and NLP are used in this work to implement Text Summarization. By using this technique, the phrase's overall meaning is enhanced and the reader's comprehension is enhanced.

This Various text summarization methods, such as extractive, abstractive, and human abstraction concepts have been compared in terms of performance, each with its specialties and limitations. This research analyses comparisons among the methods and some of their techniques used in text summarization. Our initial contribution is to suggest a thorough overview of the methods. The research methodology aims to compare text summarization methods through a systematic literature review to understand the topic and select appropriate methods. The search method involves keyword-based and citation-based techniques using academic search engines. The comparison of methods will consider various evaluation criteria such as document structure, content importance, quantitative approach, qualitative approach, dependency on machine learning, sentence generation, central concept identification, human involvement, representation in mathematics, and historical approaches. The methods would be evaluated based on these criteria to provide an objective and comprehensive comparison. No method consistently produces accurate text summaries. The best course of action will depend on the particulars and constraints of the current work because each method has both positive and negative aspects. The two primary methods for text summarization were discovered to be extractive and abstractive. This comparison study analysed various text summary and revealing each method's positive attributes and drawbacks. By giving a comprehensive overview of the main two methods, this comparative analysis advances the subject of text summarizing.

GPT-3 is a neuronal language model that performs tasks such as classification, question-answering and text summarization. Although chatbots like BlenderBot-3 work well in a conversational sense, and GPT-3 can assist experts in evaluating questions, they are quantifiably worse than real teachers in several pedagogical dimensions. We present the first systematic literature review that analyzes the main contributions and uses of GPT-3 in the field of education. The protocols suggested in the PRISMA 2020 statement were followed for the drafting of the review. According to the results, 34 significant productions were identified through a systematic search in ISI Web of Science, SCOPUS and Google Scholar. GPT-3 has been considered in the academic, ethical and medical fields, in humanities and in computer science, in the formulation of questions and answers, and through cooperative educational dialogs. GPT-3 has been proven to have valuable applications in education, such as the automation of routine tasks, in making quick diagnoses of the students' weaknesses and in the automatic generation of questions, but it still faces challenges and limitations that require additional investigation. We discuss the educational possibilities and the limitations to the use of GPT-3. [ABSTRACT FROM AUTHOR]

Fine-tuning a pre-trained sequence-to-sequence-based language model has significantly advanced the field of abstractive summarization. However, the early models of abstractive summarization were limited by the gap between training and inference, and they did not fully utilize the potential of the language model. Recent studies have introduced a two-stage framework that allows the second-stage model to re-rank the candidate summary generated by the first-stage model, to resolve these limitations. In this study, we point out that the supervision method performed in the existing re-ranking model of the two-stage abstractive summarization framework cannot learn detailed and complex information of the data. In addition, we present the problem of positional bias in the existing encoder–decoder-based re-ranking model. To address these two limitations, this study proposes a hierarchical supervision method that jointly performs summary and sentence-level supervision. For sentence-level supervision, we designed two sentence-level loss functions: intra- and inter-intra-sentence ranking losses. Compared to the existing abstractive summarization model, the proposed method exhibited a performance improvement for both the CNN/DM and XSum datasets. The proposed model outperformed the baseline model under a few-shot setting. [ABSTRACT FROM AUTHOR]

Text summarization (TS) plays a crucial role in natural language processing (NLP) by automatically condensing and capturing key information from text documents. Its significance extends to diverse fields, including engineering, healthcare, and others, where it offers substantial time and resource savings. However, manual summarization is a laborious task, prompting the need for automated text summarization systems. In this paper, we propose a novel strategy for extractive summarization that leverages a generative adversarial network (GAN)-based method and Bidirectional Encoder Representations from Transformers (BERT) word embedding. BERT, a transformer-based architecture, processes sentence bidirectionally, considering both preceding and following words. This contextual understanding empowers BERT to generate word representations that carry a deeper meaning and accurately reflect their usage within specific contexts. Our method adopts a generator and discriminator within the GAN framework. The generator assesses the likelihood of each sentence in the summary while the discriminator evaluates the generated summary. To extract meaningful features in parallel, we introduce three dilated convolution layers in the generator and discriminator. Dilated convolution allows for capturing a larger context and incorporating long-range dependencies. By introducing gaps between filter weights, dilated convolution expands the receptive field, enabling the model to consider a broader context of words. To encourage the generator to explore diverse sentence combinations that lead to high-quality summaries, we introduce various noises to each document within our proposed GAN. This approach allows the generator to learn from a range of sentence permutations and select the most suitable ones. We evaluate the performance of our proposed model using the CNN/Daily Mail dataset. The results, measured using the ROUGE metric, demonstrate the superiority of our approach compared to other tested methods. This confirms the effectiveness of our GAN-based strategy, which integrates dilated convolution layers, BERT word embedding, and a generator-discriminator framework in achieving enhanced extractive summarization performance. [ABSTRACT FROM AUTHOR]

Recently, large pretrained language models (PLMs) have led a revolution in the information retrieval community. In most PLMs‐based retrieval frameworks, the ranking performance broadly depends on the model structure and the semantic complexity of the input text. Sequence‐to‐sequence generative models for question answering or text generation have proven to be competitive, so we wonder whether these models can improve ranking effectiveness by enhancing input semantics. This article introduces SE‐BERT, a semantically enhanced bidirectional encoder representation from transformers (BERT) based ranking framework that captures more semantic information by modifying the input text. SE‐BERT utilizes a pretrained generative language model to summarize both sides of the candidate passage and concatenate them into a new input sequence, allowing BERT to acquire more semantic information within the constraints of the input sequence's length. Experimental results from two Text Retrieval Conference datasets demonstrate that our approach's effectiveness increasing as the length of the input text increases. [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]

ChatGPT has drawn considerable attention from both the general public and domain experts with its remarkable text generation capabilities. This has subsequently led to the emergence of diverse applications in the field of biomedicine and health. In this work, we examine the diverse applications of large language models (LLMs), such as ChatGPT, in biomedicine and health. Specifically, we explore the areas of biomedical information retrieval, question answering, medical text summarization, information extraction and medical education and investigate whether LLMs possess the transformative power to revolutionize these tasks or whether the distinct complexities of biomedical domain presents unique challenges. Following an extensive literature survey, we find that significant advances have been made in the field of text generation tasks, surpassing the previous state-of-the-art methods. For other applications, the advances have been modest. Overall, LLMs have not yet revolutionized biomedicine, but recent rapid progress indicates that such methods hold great potential to provide valuable means for accelerating discovery and improving health. We also find that the use of LLMs, like ChatGPT, in the fields of biomedicine and health entails various risks and challenges, including fabricated information in its generated responses, as well as legal and privacy concerns associated with sensitive patient data. We believe this survey can provide a comprehensive and timely overview to biomedical researchers and healthcare practitioners on the opportunities and challenges associated with using ChatGPT and other LLMs for transforming biomedicine and health. [ABSTRACT FROM AUTHOR]

Large language models (LLMs) have substantially pushed artificial intelligence (AI) research and applications in the last few years. They are currently able to achieve high effectiveness in different natural language processing (NLP) tasks, such as machine translation, named entity recognition, text classification, question answering, or text summarization. Recently, significant attention has been drawn to OpenAI's GPT models' capabilities and extremely accessible interface. LLMs are nowadays routinely used and studied for downstream tasks and specific applications with great success, pushing forward the state of the art in almost all of them. However, they also exhibit impressive inference capabilities when used off the shelf without further training. In this paper, we aim to study the behavior of pre‐trained language models (PLMs) in some inference tasks they were not initially trained for. Therefore, we focus our attention on very recent research works related to the inference capabilities of PLMs in some selected tasks such as factual probing and common‐sense reasoning. We highlight relevant achievements made by these models, as well as some of their current limitations that open opportunities for further research. This article is categorized under:Fundamental Concepts of Data and Knowledge > Key Design Issues in Data MiningTechnologies > Artificial Intelligence [ABSTRACT FROM AUTHOR]

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]

Query‐focused multi‐document summarization (Qf‐MDS) is a sub‐task of automatic text summarization that aims to extract a substitute summary from a document cluster of the same topic and based on a user query. Unlike other summarization tasks, Qf‐MDS has specific research challenges including the differences and similarities across related document sets, the high degree of redundancy inherent in the summaries created from multiple related sources, relevance to the given query, topic diversity in the produced summary and the small source‐to‐summary compression ratio. In this work, we propose a semantic diversity feature based query‐focused extractive summarizer (SDbQfSum) built on powerful text semantic representation techniques underpinned with Wikipedia commonsense knowledge in order to address the query‐relevance, centrality, redundancy and diversity challenges. Specifically, a semantically parsed document text is combined with knowledge‐based vectorial representation to extract effective sentence importance and query‐relevance features. The proposed monolingual summarizer is evaluated on a standard English dataset for automatic query‐focused summarization tasks, that is, the DUC2006 dataset. The obtained results show that our summarizer outperforms most state‐of‐the‐art related approaches on one or more ROUGE measures achieving 0.418, 0.092 and 0.152 in ROUGE‐1, ROUGE‐2, and ROUGE‐SU4 respectively. It also attains competitive performance with the slightly outperforming system(s), for example, the difference between our system's result and best system in ROUGE‐1 is just 0.006. We also found through the conducted experiments that our proposed custom cluster merging algorithm significantly reduces information redundancy while maintaining topic diversity across documents. [ABSTRACT FROM AUTHOR]