  Fully understanding narratives often requires identifying events in the
context of whole documents and modeling the event relations. However,
document-level event extraction is a challenging task as it requires the
extraction of event and entity coreference, and capturing arguments that span
across different sentences. Existing works on event extraction usually confine
on extracting events from single sentences, which fail to capture the
relationships between the event mentions at the scale of a document, as well as
the event arguments that appear in a different sentence than the event trigger.
In this paper, we propose an end-to-end model leveraging Deep Value Networks
(DVN), a structured prediction algorithm, to efficiently capture cross-event
dependencies for document-level event extraction. Experimental results show
that our approach achieves comparable performance to CRF-based models on ACE05,
while enjoys significantly higher computational efficiency.
