{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/discourse-as-a-function-of-event-profiling","title":"Discourse as a Function of Event: Profiling Discourse Structure in News Articles around the Main Event","arxiv_id":null,"date":"2020-07-01","proceeding":"ACL 2020 6","authors":["Prafulla Kumar Choubey","Aaron Lee","Ruihong Huang","Lu Wang"],"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.","url_abs":"https://aclanthology.org/2020.acl-main.478","url_pdf":"https://aclanthology.org/2020.acl-main.478.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"argument-mining","task_name":"Argument Mining"},{"task_slug":"articles","task_name":"Articles"},{"task_slug":"coreference-resolution","task_name":"Coreference Resolution"},{"task_slug":"event-coreference-resolution","task_name":"Event Coreference Resolution"},{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"coreference-resolution-1","task_name":"coreference-resolution"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/text-classification-on-newsdiscourse","task":"Text Classification","dataset":"NewsDiscourse","model":"Document LSTM + Document encoding (Choubey et al., 2020)","rank_in_archive_order":5,"of":8,"metrics":{"macro F1":"54.4"},"uses_additional_data":false},{"leaderboard":"/sota/text-classification-on-newsdiscourse","task":"Text Classification","dataset":"NewsDiscourse","model":"CRF Fine-grained (Choubey et al., 2020)","rank_in_archive_order":6,"of":8,"metrics":{"macro F1":"52.9"},"uses_additional_data":false},{"leaderboard":"/sota/text-classification-on-newsdiscourse","task":"Text Classification","dataset":"NewsDiscourse","model":"Feature-based (SVM) (Choubey et al., 2020)","rank_in_archive_order":8,"of":8,"metrics":{"macro F1":"38.3"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}