Examining the State-of-the-Art in News Timeline Summarization

Demian Gholipour Ghalandari, Georgiana Ifrim


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 stateof-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.
Anthology ID:
2020.acl-main.122
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1322–1334
Language:
URL:
https://www.aclweb.org/anthology/2020.acl-main.122
DOI:
10.18653/v1/2020.acl-main.122
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.acl-main.122.pdf
Video:
 http://slideslive.com/38929106