  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.
