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Survey

A Survey of Text Summarization Approaches Based on Deep Learning

Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
University of Chinese Academy of Sciences, Beijing 100049, China
Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences, Beijing 100190, China
Key Laboratory of Management, Decision and Information System, Chinese Academy of Sciences, Beijing 100190, China
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Abstract

Automatic text summarization (ATS) has achieved impressive performance thanks to recent advances in deep learning (DL) and the availability of large-scale corpora. The key points in ATS are to estimate the salience of information and to generate coherent results. Recently, a variety of DL-based approaches have been developed for better considering these two aspects. However, there is still a lack of comprehensive literature review for DL-based ATS approaches. The aim of this paper is to comprehensively review significant DL-based approaches that have been proposed in the literature with respect to the notion of generic ATS tasks and provide a walk-through of their evolution. We first give an overview of ATS and DL. The comparisons of the datasets are also given, which are commonly used for model training, validation, and evaluation. Then we summarize single-document summarization approaches. After that, an overview of multi-document summarization approaches is given. We further analyze the performance of the popular ATS models on common datasets. Various popular approaches can be employed for different ATS tasks. Finally, we propose potential research directions in this fast-growing field. We hope this exploration can provide new insights into future research of DL-based ATS.

Keywords

automatic text summarizationartificial intelligencedeep learningattentional encoder-decodernatural language processing

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Journal of Computer Science and Technology
Volume 36 Issue 3,
May 2021
Pages 633-663
DOI: 10.1007/s11390-020-0207-x
Cite this article:
Hou S-L, Huang X-K, Fei C-Q, et al. A Survey of Text Summarization Approaches Based on Deep Learning. Journal of Computer Science and Technology, 2021, 36(3): 633-663. https://doi.org/10.1007/s11390-020-0207-x
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