Abstractive summarization with the aid of extractive summarization

Yangbin Chen, Yun Ma, Xudong Mao, Qing Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)


Currently the abstractive method and extractive method are two main approaches for automatic document summarization. To fully integrate the relatedness and advantages of both approaches, we propose in this paper a general framework for abstractive summarization which incorporates extractive summarization as an auxiliary task. In particular, our framework is composed of a shared hierarchical document encoder, an attention-based decoder for abstractive summarization, and an extractor for sentence-level extractive summarization. Learning these two tasks jointly with the shared encoder allows us to better capture the semantics in the document. Moreover, we constrain the attention learned in the abstractive task by the salience estimated in the extractive task to strengthen their consistency. Experiments on the CNN/DailyMail dataset demonstrate that both the auxiliary task and the attention constraint contribute to improve the performance significantly, and our model is comparable to the state-of-the-art abstractive models.

Original languageEnglish
Title of host publicationWeb and Big Data - Second International Joint Conference, APWeb-WAIM 2018, Proceedings
EditorsJianliang Xu, Yoshiharu Ishikawa, Yi Cai
Number of pages13
ISBN (Print)9783319968896
Publication statusPublished - 1 Jan 2018
Externally publishedYes
Event2nd Asia Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data, APWeb-WAIM 2018 - Macau, China
Duration: 23 Jul 201825 Jul 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10987 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference2nd Asia Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data, APWeb-WAIM 2018


  • Abstractive document summarization
  • Joint learning
  • Squence-to-sequence

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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