Archive TimeLine Summarization (ATLS): Conceptual Framework for Timeline Generation over Historical Document Collections

Nicolas Gutehrlé, Antoine Doucet, Adam Jatowt


Abstract
Archive collections are nowadays mostly available through search engines interfaces, which allow a user to retrieve documents by issuing queries. The study of these collections may be, however, impaired by some aspects of search engines, such as the overwhelming number of documents returned or the lack of contextual knowledge provided. New methods that could work independently or in combination with search engines are then required to access these collections. In this position paper, we propose to extend TimeLine Summarization (TLS) methods on archive collections to assist in their studies. We provide an overview of existing TLS methods and we describe a conceptual framework for an Archive TimeLine Summarization (ATLS) system, which aims to generate informative, readable and interpretable timelines.
Anthology ID:
2022.latechclfl-1.3
Volume:
Proceedings of the 6th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
LaTeCHCLfL
SIG:
SIGHUM
Publisher:
International Conference on Computational Linguistics
Note:
Pages:
13–23
Language:
URL:
https://aclanthology.org/2022.latechclfl-1.3
DOI:
Bibkey:
Cite (ACL):
Nicolas Gutehrlé, Antoine Doucet, and Adam Jatowt. 2022. Archive TimeLine Summarization (ATLS): Conceptual Framework for Timeline Generation over Historical Document Collections. In Proceedings of the 6th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pages 13–23, Gyeongju, Republic of Korea. International Conference on Computational Linguistics.
Cite (Informal):
Archive TimeLine Summarization (ATLS): Conceptual Framework for Timeline Generation over Historical Document Collections (Gutehrlé et al., LaTeCHCLfL 2022)
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PDF:
https://aclanthology.org/2022.latechclfl-1.3.pdf