Keywords-Guided Abstractive Sentence Summarization


  • Haoran Li JD AI Research
  • Junnan Zhu Chinese Academy of Sciences
  • Jiajun Zhang Chinese Academy of Sciences
  • Chengqing Zong Chinese Academy of Sciences
  • Xiaodong He JD AI Research



We study the problem of generating a summary for a given sentence. Existing researches on abstractive sentence summarization ignore that keywords in the input sentence provide significant clues for valuable content, and humans tend to write summaries covering these keywords. In this paper, we propose an abstractive sentence summarization method by applying guidance signals of keywords to both the encoder and the decoder in the sequence-to-sequence model. A multi-task learning framework is adopted to jointly learn to extract keywords and generate a summary for the input sentence. We apply keywords-guided selective encoding strategies to filter source information by investigating the interactions between the input sentence and the keywords. We extend pointer-generator network by a dual-attention and a dual-copy mechanism, which can integrate the semantics of the input sentence and the keywords, and copy words from both the input sentence and the keywords. We demonstrate that multi-task learning and keywords-oriented guidance facilitate sentence summarization task, achieving better performance than the competitive models on the English Gigaword sentence summarization dataset.




How to Cite

Li, H., Zhu, J., Zhang, J., Zong, C., & He, X. (2020). Keywords-Guided Abstractive Sentence Summarization. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 8196-8203.



AAAI Technical Track: Natural Language Processing