Discourse as a Function of Event: Profiling Discourse Structure in News Articles around the Main Event

This repository contains the data and code for the ACL 2020 paper: Discourse as a Function of Event: Profiling Discourse Structure in News Articles around the Main Event. If you use the dataset or model in your work, please cite the following.

@inproceedings{choubey-etal-2020-discourse,
    title = "Discourse as a Function of Event: Profiling Discourse Structure in News Articles around the Main Event",
    author = "Choubey, Prafulla Kumar  and
      Lee, Aaron  and
      Huang, Ruihong  and
      Wang, Lu",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.478",
    pages = "5374--5386",
    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.",
}