Examining the State-of-the-Art in News Timeline Summarization
|Title:||Examining the State-of-the-Art in News Timeline Summarization||Authors:||Ghalandari, Demian Gholipour; Ifrim, Georgiana||Permanent link:||http://hdl.handle.net/10197/12035||Date:||8-Jul-2020||Online since:||2021-03-11T14:13:10Z||Abstract:||Previous work on automatic news timeline summarization (TLS) leaves an unclear picture about how this task can generally be approached and how well it is currently solved. This is mostly due to the focus on individual subtasks, such as date selection and date summarization, and to the previous lack of appropriate evaluation metrics for the full TLS task. In this paper, we compare different TLS strategies using appropriate evaluation frameworks, and propose a simple and effective combination of methods that improves over the state-of-the-art on all tested benchmarks. For a more robust evaluation, we also present a new TLS dataset, which is larger and spans longer time periods than previous datasets.||Funding Details:||Irish Research Council
Science Foundation Ireland
|Type of material:||Conference Publication||Keywords:||News topics; News events; Timeline summarization||DOI:||10.18653/v1/2020.acl-main.122||Other versions:||https://acl2020.org/||Language:||en||Status of Item:||Peer reviewed||Is part of:||Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics||Conference Details:||The 58th Annual Meeting of the Association for Computational Linguistics, Online, 6-8 July 2020||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by/3.0/ie/|
|Appears in Collections:||Computer Science Research Collection|
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