Papers › Discourse as a Function of Event: Profiling Discourse Structure in News Articles...
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
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Text Classification | NewsDiscourse | Document LSTM + Document encoding (Choubey et al., 2020) | macro F1 | 54.4 | #5 of 8 | Archive leaderboard | report |
| Text Classification | NewsDiscourse | CRF Fine-grained (Choubey et al., 2020) | macro F1 | 52.9 | #6 of 8 | Archive leaderboard | report |
| Text Classification | NewsDiscourse | Feature-based (SVM) (Choubey et al., 2020) | macro F1 | 38.3 | #8 of 8 | Archive leaderboard | report |
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