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://aclanthology.org/2020.coling-main.10
- DOI:
- 10.18653/v1/2020.coling-main.10
- Cite (ACL):
- Disha Jindal, Daniel Deutsch, and Dan Roth. 2020. Is Killed More Significant than Fled? A Contextual Model for Salient Event Detection. In Proceedings of the 28th International Conference on Computational Linguistics, pages 114–124, Barcelona, Spain (Online). International Committee on Computational Linguistics.
- Cite (Informal):
- Is Killed More Significant than Fled? A Contextual Model for Salient Event Detection (Jindal et al., COLING 2020)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2020.coling-main.10.pdf