Harvesting Events from Multiple Sources: Towards a Cross-Document Event Extraction Paradigm
This addresses the need for comprehensive event extraction across documents, though it is incremental as it builds on existing extraction methods.
The paper tackles the problem of limited and biased event information in single-document event extraction by proposing cross-document event extraction (CDEE) to integrate data from multiple sources, achieving about 72% F1 on a new dataset with over 70% cross-document events.
Document-level event extraction aims to extract structured event information from unstructured text. However, a single document often contains limited event information and the roles of different event arguments may be biased due to the influence of the information source. This paper addresses the limitations of traditional document-level event extraction by proposing the task of cross-document event extraction (CDEE) to integrate event information from multiple documents and provide a comprehensive perspective on events. We construct a novel cross-document event extraction dataset, namely CLES, which contains 20,059 documents and 37,688 mention-level events, where over 70% of them are cross-document. To build a benchmark, we propose a CDEE pipeline that includes 5 steps, namely event extraction, coreference resolution, entity normalization, role normalization and entity-role resolution. Our CDEE pipeline achieves about 72% F1 in end-to-end cross-document event extraction, suggesting the challenge of this task. Our work builds a new line of information extraction research and will attract new research attention.