Abstract
Understanding discourse structures of news articles is vital to effectively contextualize the occurrence of a news event. To enable computational modeling of news structures, we apply an existing theory of functional discourse structure for news articles that revolves around the main event and create a human-annotated corpus of 802 documents spanning over four domains and three media sources. Next, we propose several document-level neural-network models to automatically construct news content structures. Finally, we demonstrate that incorporating system predicted news structures yields new state-of-the-art performance for event coreference resolution. The news documents we annotated are openly available and the annotations are publicly released for future research.- Anthology ID:
- 2020.acl-main.478
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5374–5386
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.478
- DOI:
- 10.18653/v1/2020.acl-main.478
- Cite (ACL):
- Prafulla Kumar Choubey, Aaron Lee, Ruihong Huang, and Lu Wang. 2020. Discourse as a Function of Event: Profiling Discourse Structure in News Articles around the Main Event. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5374–5386, Online. Association for Computational Linguistics.
- Cite (Informal):
- Discourse as a Function of Event: Profiling Discourse Structure in News Articles around the Main Event (Choubey et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.acl-main.478.pdf