Enhancing Document-Level Event Extraction via Structure-Aware Heterogeneous Graph with Multi-Granularity Subsentences

Yuhan Liu, Neng Gao, Yifei Zhang, Zhe Kong

Abstract: Document-level Event Extraction aims to identify events from an entire article. It is quite a challenging task because event arguments scatter across several sentences and multiple events in a document may have influence on each other. Previous methods, however, did not take advantage of document structures that have been proved to be effective for sentence-level event extraction. In this work, we propose a structure-aware heterogeneous graph with subsentences for document-level event extraction. Firstly, we build a syntactic graph to capture long-range dependencies between cross-sentence event arguments. Then, multi-granularity sub-sentences are added into the graph to acquire fine-grained understanding. Finally, a global memory stores extracted events so that interactions among multiple events can be captured. Extensive experiments demonstrate that our model outperforms the state of the art models on a widely used large-scale document-level event extraction dataset.