CitationIE: Leveraging the Citation Graph for Scientific Information Extraction

TL;DR

This paper introduces CitationIE, a method that enhances scientific information extraction by incorporating citation graph data, leading to improved accuracy over existing content-only approaches.

Contribution

It is the first to leverage citation graph structure alongside text content for scientific information extraction, significantly improving performance.

Findings

Citation graph integration improves extraction accuracy

Combining graph and text yields state-of-the-art results

Software tools are released for citation-aware extraction

Abstract

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.