Utilize Discourse Relations to Segment Document for Effective Summarization

Li Jiazheng, Muhammad Rafi

    Research output: Chapter in Book/Published conference outputConference publication

    Abstract

    This paper proposes a clause-based extractive summarization algorithm by ranking and extracting semantic clauses from the original document. Discourse structure relation is useful for identifying semantically important parts of the source document. We segment the document into clauses and evaluate the importance of clauses based on semantic relations, and then, rank and extract them coarsely, and utilize graph rank to refine the extracted clauses. This way can create a more concise summary with more information and less redundancy. Research reach the following results: 1) compared with the other summarization algorithms on different granularity, the clausebased summarization achieves higher recall score; and, 2) different discourse relations have different importance.
    Original languageEnglish
    Title of host publicationProceedings - 15th International Conference on Semantics, Knowledge and Grids
    Subtitle of host publicationOn Big Data, AI and Future Interconnection Environment, SKG 2019
    EditorsHai Zhuge, Xiaoping Sun
    PublisherIEEE
    Pages12-15
    Number of pages4
    ISBN (Electronic)978-1-7281-5823-5
    ISBN (Print)978-1-7281-5824-2
    DOIs
    Publication statusPublished - 23 Mar 2020
    Event2019 15th International Conference on Semantics, Knowledge and Grids (SKG) - Guangzhou, China
    Duration: 17 Sept 201918 Sept 2019

    Publication series

    NameProceedings - 15th International Conference on Semantics, Knowledge and Grids: On Big Data, AI and Future Interconnection Environment, SKG 2019

    Conference

    Conference2019 15th International Conference on Semantics, Knowledge and Grids (SKG)
    Period17/09/1918/09/19

    Keywords

    • Discourse structure
    • Semantic link network
    • Text summarization

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