Discourse as a Function of Event: Profiling Discourse Structure in News Articles around the Main Event
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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Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
---|---|---|---|---|---|---|
Text Classification | NewsDiscourse | Document LSTM + Document encoding (Choubey et al., 2020) | macro F1 | 54.4 | # 5 | |
Text Classification | NewsDiscourse | Feature-based (SVM) (Choubey et al., 2020) | macro F1 | 38.3 | # 8 | |
Text Classification | NewsDiscourse | CRF Fine-grained (Choubey et al., 2020) | macro F1 | 52.9 | # 6 |