Tran, G, Herder, E and Markert, K (2015) Joint Graphical Models for Date Selection in Timeline Summarization. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics. ACL-IJCNLP 2015, 26-31 Jul 2015, Beijing, China. Association for Computational Linguistics , 1598 - 1607. ISBN 978-1-941643-72-3
Abstract
Automatic timeline summarization (TLS) generates precise, dated overviews over (often prolonged) events, such as wars or economic crises. One subtask of TLS selects the most important dates for an event within a certain time frame. Date selection has up to now been handled via supervised machine learning approaches that estimate the importance of each date separately, using features such as the frequency of date mentions in news corpora. This approach neglects interactions between different dates that occur due to connections between subevents. We therefore suggest a joint graphical model for date selection. Even unsupervised versions of this model perform as well as supervised state-of-theart approaches. With parameter tuning on training data, it outperforms prior supervised models by a considerable margin.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2015, Association for Computational Linguistics. This is an author produced version of a paper published in Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics. |
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) > Artificial Intelligence & Biological Systems (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 13 Nov 2015 10:46 |
Last Modified: | 16 Jan 2018 12:21 |
Published Version: | http://www.aclweb.org/anthology/P/P15/ |
Status: | Published |
Publisher: | Association for Computational Linguistics |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:91931 |