Discourse as a Function of Event: Profiling Discourse Structure in News Articles around the Main Event
Prafulla Kumar Choubey, Aaron Lee, Ruihong Huang, Lu Wang
Discourse and Pragmatics Long Paper
Session 9B: Jul 7
(18:00-19:00 GMT)
Session 10B: Jul 7
(21:00-22:00 GMT)
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.
You can open the
pre-recorded video
in a separate window.
NOTE: The SlidesLive video may display a random order of the authors.
The correct author list is shown at the top of this webpage.
Similar Papers
Fine-grained Interest Matching for Neural News Recommendation
Heyuan Wang, Fangzhao Wu, Zheng Liu, Xing Xie,

NSTM: Real-Time Query-Driven News Overview Composition at Bloomberg
Joshua Bambrick, Minjie Xu, Andy Almonte, Igor Malioutov, Guim Perarnau, Vittorio Selo, Iat Chong Chan,

MIND: A Large-scale Dataset for News Recommendation
Fangzhao Wu, Ying Qiao, Jiun-Hung Chen, Chuhan Wu, Tao Qi, Jianxun Lian, Danyang Liu, Xing Xie, Jianfeng Gao, Winnie Wu, Ming Zhou,
