Journal of Computer Science and Technology ›› 2021, Vol. 36 ›› Issue (3): 633-663.doi: 10.1007/s11390-020-0207-x

Special Issue: Artificial Intelligence and Pattern Recognition

• Regular Paper • Previous Articles     Next Articles

A Survey of Text Summarization Approaches Based on Deep Learning

Sheng-Luan Hou1,2, Xi-Kun Huang2,3,4, Chao-Qun Fei1,2, Shu-Han Zhang1,2, Yang-Yang Li3,4, Qi-Lin Sun2,3,4, and Chuan-Qing Wang2,3,4        

  1. 1 Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China;
    2 University of Chinese Academy of Sciences, Beijing 100049, China;
    3 Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences, Beijing 100190, China;
    4 Key Laboratory of Management, Decision and Information System, Chinese Academy of Sciences, Beijing 100190, China
  • Received:2019-12-06 Revised:2020-12-24 Online:2021-05-05 Published:2021-05-31
  • About author:Sheng-Luan Hou received his M.S. degree in mathematics from Beijing University of Technology, Beijing, in 2014. He is now a Ph.D. candidate in Institute of Computing Technology (ICT), Chinese Academy of Sciences, Beijing. His research interests include deep learning, automatic text summarization, and artificial intelligence.
  • Supported by:
    The work was supported by the National Key Research and Development Program of China under Grant No. 2016YFB1000902 and the National Natural Science Foundation of China under Grant Nos. 61232015, 61472412, and 61621003.

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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.

Key words: automatic text summarization; artificial intelligence; deep learning; attentional encoder-decoder; natural language processing;

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