Abstractive Multi-Document Summarization based on Semantic Link Network

Abstract

The key to realize advanced document summarization is semantic representation of documents. This paper investigates the role of Semantic Link Network in representing and understanding documents for multi-document summarization. It proposes a novel abstractive multi-document summarization framework by first transforming documents into a Semantic Link Network of concepts and events and then transforming the Semantic Link Network into the summary of the documents based on the selection of important concepts and events while keeping semantics coherence. Experiments on benchmark datasets show that the proposed summarization approach significantly outperforms relevant state-of-the-art baselines and the Semantic Link Network plays an important role in representing and understanding documents.

Publication DOI: https://doi.org/10.1109/TKDE.2019.2922957
Divisions: College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Computer Science
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
College of Engineering & Physical Sciences
Additional Information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords: Abstractive summarization,information extraction,multi-document summarization,semantic link network,Information Systems,Computer Science Applications,Computational Theory and Mathematics
Publication ISSN: 1558-2191
Full Text Link:
Related URLs: https://ieeexpl ... ocument/8736808 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2021-01-01
Published Online Date: 2019-06-14
Accepted Date: 2019-06-01
Authors: Li, Wei
Zhuge, Hai (ORCID Profile 0000-0001-8250-6408)

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