Mao, Q, Li, J, Wang, J et al. (4 more authors) (2022) Explicitly Modeling Importance and Coherence for Timeline Summarization. In: Proceedings of ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 23-27 May 2022, Singapore. IEEE , pp. 8062-8066. ISBN 978-1-6654-0540-9
Abstract
Timeline summarization (TLS) identifies major events and generates short summaries on how the event evolves in a period of time. Existing timeline summarization methods generate summaries by considering the coverage and diversity of the content and temporized information but ignore the importance and coherence of sentences used in summary. However, ignoring such information often causes missing important facts in the generated TLS and confuses users. We propose a better approach for TLS by explicitly optimizing importance and coherence on top of coverage and diversity. We apply our approach to both direct and pipeline TLS frameworks. Experimental results show that our approach achieves better performance when compared with two state-of-the-art TLS methods.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 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. |
Keywords: | Timeline, text summarization, text mining |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 18 Feb 2022 15:32 |
Last Modified: | 07 Aug 2023 11:32 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ICASSP43922.2022.9746383 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:183688 |