Distinguishing Between Foreground and Background Events in News

Mohammed Aldawsari, Adrian Perez, Deya Banisakher, Mark Finlayson


Abstract
Determining whether an event in a news article is a foreground or background event would be useful in many natural language processing tasks, for example, temporal relation extraction, summarization, or storyline generation. We introduce the task of distinguishing between foreground and background events in news articles as well as identifying the general temporal position of background events relative to the foreground period (past, present, future, and their combinations). We achieve good performance (0.73 F1 for background vs. foreground and temporal position, and 0.79 F1 for background vs. foreground only) on a dataset of news articles by leveraging discourse information in a featurized model. We release our implementation and annotated data for other researchers
Anthology ID:
2020.coling-main.453
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Donia Scott, Nuria Bel, Chengqing Zong
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
5171–5180
Language:
URL:
https://aclanthology.org/2020.coling-main.453
DOI:
10.18653/v1/2020.coling-main.453
Bibkey:
Cite (ACL):
Mohammed Aldawsari, Adrian Perez, Deya Banisakher, and Mark Finlayson. 2020. Distinguishing Between Foreground and Background Events in News. In Proceedings of the 28th International Conference on Computational Linguistics, pages 5171–5180, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Distinguishing Between Foreground and Background Events in News (Aldawsari et al., COLING 2020)
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PDF:
https://preview.aclanthology.org/nschneid-patch-1/2020.coling-main.453.pdf