ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2021, Vol. 58 ›› Issue (1): 1-21.doi: 10.7544/issn1000-1239.2021.20190785

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Survey on Automatic Text Summarization

Li Jinpeng1,2, Zhang Chuang1, Chen Xiaojun1, Hu Yue1,2, Liao Pengcheng1,2   

  1. 1(Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093);2(School of Cyber Security, University of Chinese Academy of Sciences, Beijing 100040)
  • Online:2021-01-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61602474).

Abstract: In recent years, the rapid development of Internet technology has greatly facilitated the daily life of human, and it is inevitable that massive information erupts in a blowout. How to quickly and effectively obtain the required information on the Internet is an urgent problem. The automatic text summarization technology can effectively alleviate this problem. As one of the most important fields in natural language processing and artificial intelligence, it can automatically produce a concise and coherent summary from a long text or text set through computer, in which the summary should accurately reflect the central themes of source text. In this paper, we expound the connotation of automatic summarization, review the development of automatic text summarization technique and introduce two main techniques in detail: extractive and abstractive summarization, including feature scoring, classification method, linear programming, submodular function, graph ranking, sequence labeling, heuristic algorithm, deep learning, etc. We also analyze the datasets and evaluation metrics that are commonly used in automatic summarization. Finally, the challenges ahead and the future trends of research and application have been predicted.

Key words: automatic text summarization, extractive, abstractive, deep learning, ROUGE metric

CLC Number: