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Recent automatic text summarization techniques: a survey

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Abstract

As information is available in abundance for every topic on internet, condensing the important information in the form of summary would benefit a number of users. Hence, there is growing interest among the research community for developing new approaches to automatically summarize the text. Automatic text summarization system generates a summary, i.e. short length text that includes all the important information of the document. Since the advent of text summarization in 1950s, researchers have been trying to improve techniques for generating summaries so that machine generated summary matches with the human made summary. Summary can be generated through extractive as well as abstractive methods. Abstractive methods are highly complex as they need extensive natural language processing. Therefore, research community is focusing more on extractive summaries, trying to achieve more coherent and meaningful summaries. During a decade, several extractive approaches have been developed for automatic summary generation that implements a number of machine learning and optimization techniques. This paper presents a comprehensive survey of recent text summarization extractive approaches developed in the last decade. Their needs are identified and their advantages and disadvantages are listed in a comparative manner. A few abstractive and multilingual text summarization approaches are also covered. Summary evaluation is another challenging issue in this research field. Therefore, intrinsic as well as extrinsic both the methods of summary evaluation are described in detail along with text summarization evaluation conferences and workshops. Furthermore, evaluation results of extractive summarization approaches are presented on some shared DUC datasets. Finally this paper concludes with the discussion of useful future directions that can help researchers to identify areas where further research is needed.

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Notes

  1. http://champollion.sourceforge.net/.

  2. http://www-nlpir.nist.gov/related_projects/tipster_summac/.

  3. http://research.nii.ac.jp/ntcir/outline/prop-en.html.

  4. http://www-nlpir.nist.gov/projects/duc/.

  5. http://www.isi.edu/licensed-sw/see/.

  6. http://www.nist.gov/tac/.

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Gambhir, M., Gupta, V. Recent automatic text summarization techniques: a survey. Artif Intell Rev 47, 1–66 (2017). https://doi.org/10.1007/s10462-016-9475-9

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