Is Killed More Significant than Fled? A Contextual Model for Salient Event Detection

Disha Jindal, Daniel Deutsch, Dan Roth


Abstract
Identifying the key events in a document is critical to holistically understanding its important information. Although measuring the salience of events is highly contextual, most previous work has used a limited representation of events that omits essential information. In this work, we propose a highly contextual model of event salience that uses a rich representation of events, incorporates document-level information and allows for interactions between latent event encodings. Our experimental results on an event salience dataset demonstrate that our model improves over previous work by an absolute 2-4% on standard metrics, establishing a new state-of-the-art performance for the task. We also propose a new evaluation metric that addresses flaws in previous evaluation methodologies. Finally, we discuss the importance of salient event detection for the downstream task of summarization.
Anthology ID:
2020.coling-main.10
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
114–124
Language:
URL:
https://www.aclweb.org/anthology/2020.coling-main.10
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.coling-main.10.pdf