  Automatically extracting key information from scientific documents has the
potential to help scientists work more efficiently and accelerate the pace of
scientific progress. Prior work has considered extracting document-level entity
clusters and relations end-to-end from raw scientific text, which can improve
literature search and help identify methods and materials for a given problem.
Despite the importance of this task, most existing works on scientific
information extraction (SciIE) consider extraction solely based on the content
of an individual paper, without considering the paper's place in the broader
literature. In contrast to prior work, we augment our text representations by
leveraging a complementary source of document context: the citation graph of
referential links between citing and cited papers. On a test set of
English-language scientific documents, we show that simple ways of utilizing
the structure and content of the citation graph can each lead to significant
gains in different scientific information extraction tasks. When these tasks
are combined, we observe a sizable improvement in end-to-end information
extraction over the state-of-the-art, suggesting the potential for future work
along this direction. We release software tools to facilitate citation-aware
SciIE development.
