  Document-level Event Extraction (DEE) is particularly tricky due to the two
challenges it poses: scattering-arguments and multi-events. The first challenge
means that arguments of one event record could reside in different sentences in
the document, while the second one reflects one document may simultaneously
contain multiple such event records. Motivated by humans' reading cognitive to
extract information of interests, in this paper, we propose a method called HRE
(Human Reading inspired Extractor for Document Events), where DEE is decomposed
into these two iterative stages, rough reading and elaborate reading.
Specifically, the first stage browses the document to detect the occurrence of
events, and the second stage serves to extract specific event arguments. For
each concrete event role, elaborate reading hierarchically works from sentences
to characters to locate arguments across sentences, thus the
scattering-arguments problem is tackled. Meanwhile, rough reading is explored
in a multi-round manner to discover undetected events, thus the multi-events
problem is handled. Experiment results show the superiority of HRE over prior
competitive methods.
