@inproceedings{yu-etal-2021-multi,
title = "Multi-{T}ime{L}ine Summarization ({MTLS}): Improving Timeline Summarization by Generating Multiple Summaries",
author = "Yu, Yi and
Jatowt, Adam and
Doucet, Antoine and
Sugiyama, Kazunari and
Yoshikawa, Masatoshi",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "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 = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.32",
doi = "10.18653/v1/2021.acl-long.32",
pages = "377--387",
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.",
}
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%0 Conference Proceedings
%T Multi-TimeLine Summarization (MTLS): Improving Timeline Summarization by Generating Multiple Summaries
%A Yu, Yi
%A Jatowt, Adam
%A Doucet, Antoine
%A Sugiyama, Kazunari
%A Yoshikawa, Masatoshi
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S 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)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F yu-etal-2021-multi
%X 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.
%R 10.18653/v1/2021.acl-long.32
%U https://aclanthology.org/2021.acl-long.32
%U https://doi.org/10.18653/v1/2021.acl-long.32
%P 377-387
Markdown (Informal)
[Multi-TimeLine Summarization (MTLS): Improving Timeline Summarization by Generating Multiple Summaries](https://aclanthology.org/2021.acl-long.32) (Yu et al., ACL-IJCNLP 2021)
ACL