Multi-TimeLine Summarization (MTLS): Improving Timeline Summarization by Generating Multiple Summaries

Yi Yu, Adam Jatowt, Antoine Doucet, Kazunari Sugiyama, Masatoshi Yoshikawa


Abstract
In this paper, we address a novel task, Multiple TimeLine Summarization (MTLS), which extends the flexibility and versatility of Time-Line Summarization (TLS). Given any collection of time-stamped news articles, MTLS automatically discovers important yet different stories and generates a corresponding time-line for each story.To achieve this, we propose a novel unsupervised summarization framework based on two-stage affinity propagation. We also introduce a quantitative evaluation measure for MTLS based on previousTLS evaluation methods. Experimental results show that our MTLS framework demonstrates high effectiveness and MTLS task can give bet-ter results than TLS.
Anthology ID:
2021.acl-long.32
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
377–387
Language:
URL:
https://aclanthology.org/2021.acl-long.32
DOI:
10.18653/v1/2021.acl-long.32
Bibkey:
Cite (ACL):
Yi Yu, Adam Jatowt, Antoine Doucet, Kazunari Sugiyama, and Masatoshi Yoshikawa. 2021. Multi-TimeLine Summarization (MTLS): Improving Timeline Summarization by Generating Multiple Summaries. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 377–387, Online. Association for Computational Linguistics.
Cite (Informal):
Multi-TimeLine Summarization (MTLS): Improving Timeline Summarization by Generating Multiple Summaries (Yu et al., ACL 2021)
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PDF:
https://aclanthology.org/2021.acl-long.32.pdf
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 https://aclanthology.org/2021.acl-long.32.mp4