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Chinese long text summarization using improved sequence-to-sequence lstm

Journal of physics. Conference series, 2020-05, Vol.1550 (3), p.32162 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;2020. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1742-6588 ;EISSN: 1742-6596 ;DOI: 10.1088/1742-6596/1550/3/032162

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  • Title:
    Chinese long text summarization using improved sequence-to-sequence lstm
  • Author: Yao, Zanjie ; Chen, Aixiang ; Xie, Han
  • Subjects: Natural language processing
  • Is Part Of: Journal of physics. Conference series, 2020-05, Vol.1550 (3), p.32162
  • Description: Text summarization is an important issue in natural language processing. The existing method has the problem of low accuracy when performing long text summarization. In this paper, We use the LSTM to construct the sequence-to-sequence model, and combine the attention mechanism to process automatic Chinese long text summarization.The experimental results indicate that our method can accurately extract key information from long text, generate high-quality summary.
  • Publisher: Bristol: IOP Publishing
  • Language: English
  • Identifier: ISSN: 1742-6588
    EISSN: 1742-6596
    DOI: 10.1088/1742-6596/1550/3/032162
  • Source: Geneva Foundation Free Medical Journals at publisher websites
    IOPscience (Open Access)
    IOP 英国物理学会OA刊
    ProQuest Central
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