Automatic Text Summarization Methods: A Comprehensive Review

• URL: http://arxiv.org/abs/2204.01849v1
• Date: Thu, 3 Mar 2022 10:45:00 GMT
• Title: Automatic Text Summarization Methods: A Comprehensive Review
• Authors: Divakar Yadav, Jalpa Desai, Arun Kumar Yadav
• Abstract summary: This study provides a detailed analysis of text summarization concepts such as summarization approaches, techniques used, standard datasets, evaluation metrics and future scopes for research.
• Score: 1.6114012813668934
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: One of the most pressing issues that have arisen due to the rapid growth of the Internet is known as information overloading. Simplifying the relevant information in the form of a summary will assist many people because the material on any topic is plentiful on the Internet. Manually summarising massive amounts of text is quite challenging for humans. So, it has increased the need for more complex and powerful summarizers. Researchers have been trying to improve approaches for creating summaries since the 1950s, such that the machine-generated summary matches the human-created summary. This study provides a detailed state-of-the-art analysis of text summarization concepts such as summarization approaches, techniques used, standard datasets, evaluation metrics and future scopes for research. The most commonly accepted approaches are extractive and abstractive, studied in detail in this work. Evaluating the summary and increasing the development of reusable resources and infrastructure aids in comparing and replicating findings, adding competition to improve the outcomes. Different evaluation methods of generated summaries are also discussed in this study. Finally, at the end of this study, several challenges and research opportunities related to text summarization research are mentioned that may be useful for potential researchers working in this area.

Related papers

• Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback [57.816210168909286]
  We leverage recent progress on textual entailment models to address this problem for abstractive summarization systems. We use reinforcement learning with reference-free, textual entailment rewards to optimize for factual consistency. Our results, according to both automatic metrics and human evaluation, show that our method considerably improves the faithfulness, salience, and conciseness of the generated summaries.
  arXiv  Detail & Related papers  (2023-05-31T21:04:04Z)
• Lay Text Summarisation Using Natural Language Processing: A Narrative Literature Review [1.8899300124593648]
  The aim of this literature review is to describe and compare the different text summarisation approaches used to generate lay summaries. We screened 82 articles and included eight relevant papers published between 2020 and 2021, using the same dataset. A combination of extractive and abstractive summarisation methods in a hybrid approach was found to be most effective.
  arXiv  Detail & Related papers  (2023-03-24T18:30:50Z)
• Human-in-the-loop Abstractive Dialogue Summarization [61.4108097664697]
  We propose to incorporate different levels of human feedback into the training process. This will enable us to guide the models to capture the behaviors humans care about for summaries.
  arXiv  Detail & Related papers  (2022-12-19T19:11:27Z)
• Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature [21.440724685950443]
  We present two novel lay summarisation datasets, PLOS (large-scale) and eLife (medium-scale) We provide a thorough characterisation of our lay summaries, highlighting differing levels of readability and abstractiveness between datasets.
  arXiv  Detail & Related papers  (2022-10-18T15:28:30Z)
• Survey of Query-based Text Summarization [31.907523097592513]
  query-based text summarization is an important real world problem that requires to condense the prolix text data into a summary under the guidance of the query information. This survey aims at summarizing some interesting work in query-based text summarization methods as well as related generic text summarization methods.
  arXiv  Detail & Related papers  (2022-09-17T05:34:32Z)
• An Empirical Survey on Long Document Summarization: Datasets, Models and Metrics [33.655334920298856]
  We provide a comprehensive overview of the research on long document summarization. We conduct an empirical analysis to broaden the perspective on current research progress.
  arXiv  Detail & Related papers  (2022-07-03T02:57:22Z)
• Summarization with Graphical Elements [55.5913491389047]
  We propose a new task: summarization with graphical elements. We collect a high quality human labeled dataset to support research into the task.
  arXiv  Detail & Related papers  (2022-04-15T17:16:41Z)
• AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer Summarization [73.91543616777064]
  Community Question Answering (CQA) fora such as Stack Overflow and Yahoo! Answers contain a rich resource of answers to a wide range of community-based questions. One goal of answer summarization is to produce a summary that reflects the range of answer perspectives. This work introduces a novel dataset of 4,631 CQA threads for answer summarization, curated by professional linguists.
  arXiv  Detail & Related papers  (2021-11-11T21:48:02Z)
• What Makes a Good Summary? Reconsidering the Focus of Automatic Summarization [49.600619575148706]
  We find that the current focus of the field does not fully align with participants' wishes. Based on our findings, we argue that it is important to adopt a broader perspective on automatic summarization.
  arXiv  Detail & Related papers  (2020-12-14T15:12:35Z)
• From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information [77.89755281215079]
  Text summarization is the research area aiming at creating a short and condensed version of the original document. In real-world applications, most of the data is not in a plain text format. This paper focuses on the survey of these new summarization tasks and approaches in the real-world application.
  arXiv  Detail & Related papers  (2020-05-10T14:59:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.