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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Results from the Paper


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

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