Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (3): 621-636.DOI: 10.3778/j.issn.1673-9418.2109014

• Artificial Intelligence • Previous Articles     Next Articles

Abstractive Text Summarization Model with Coherence Reinforcement and No Ground Truth Dependency

CHEN Gongchi1, RONG Huan1,+(), MA Tinghuai2   

  1. 1. School of Artificial Intelligence (School of Future Technology), Nanjing University of Information Science & Technology, Nanjing 210044, China
    2. School of Computer Science, Nanjing University of Information Science & Technology, Nanjing 210044, China;
  • Received:2021-09-06 Revised:2021-11-22 Online:2022-03-01 Published:2021-11-30
  • About author:CHEN Gongchi, born in 2000. His research interests include natural language processing, text summarization, etc.
    RONG Huan, born in 1990, Ph.D., lecturer. His research interests include social media mining, content security on social network, knowledge engineering, etc.
    MA Tinghuai, born in 1974, Ph.D., professor.His research interests include social network privacy protection, big data mining, text emotion computing, etc.
  • Supported by:
    National Natural Science Foundation of China(62102187);Natural Science Foundation of Jiangsu Province (Basic Research Program)(BK20210639);Provincial College Students Innovation and Entrepreneurship Training Program of Jiangsu Province in 2021(202110300093Y);National Key Research and Development Program of China(2021YFE0104400)


陈共驰1, 荣欢1,+(), 马廷淮2   

  1. 1.南京信息工程大学 人工智能学院(未来技术学院),南京 210044
    2.南京信息工程大学 计算机学院(软件学院、网络空间安全学院),南京 210044
  • 通讯作者: + E-mail:
  • 作者简介:陈共驰(2000—),男,四川自贡人,主要研究方向为自然语言处理、文本摘要等。
  • 基金资助:


Automatic text summarization aims to compress a given document, which can efficiently reflect the main idea of the source document with a short summary. At present, abstractive summarization method has become a research hotspot in the field of text summarization because it can paraphrase the source document with flexible and abundant vocabulary. However, existing abstractive summarization model reorganizes original words and adds new words when generating summary. That’s why it can easily cause the inconsistency and low readability. In addition, the traditional supervised learning based on labeled data requires high cost to improve the coherence of summary sentences, which limits the practical application. Therefore, this paper proposes an abstractive text summarization model with coherence reinforcement and no ground truth dependency (ATS_CG). On the one hand, based on the embdding of the source document, the model generates extractive label to describe the filtering process of the key information. And then, the filtered sentence embeddings are decoded by the decoder. On the other hand, based on the original word probability distribution output by the decoder, two types of summarization are generated according to “probability selection” and “Softmax-greedy selection”. And then, the model will compute the overall rewards of the two types of summarization from the two aspects of coherence and content. Next, the model will learn to filter key sentences and decode them through the self-critical policy gradient, so as to generate abstractive summarizaion with high coherence and quality. Experiments show that ATS_CG is superior to the existing text summarization methods in terms of evaluation scores on the whole, even without any ground truth. At the same time, abstractive summarization generated by ATS_CG is also better than the existing methods in coherence, relevance, redundancy, novelty and perplexity.

Key words: automatic text summarization, natural language processing, reinforcement learning, information retrieval and integration



关键词: 自动文本摘要, 自然语言处理, 强化学习, 信息检索与集成

CLC Number: