C itation IE : Leveraging the Citation Graph for Scientific Information Extraction (2021)

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.

## introduction the rapid expansion in published scientific knowledge has enormous potential for good, if it can only be harnessed correctly. for example, during the first five months of the global covid-19 pandemic, at least 11000 papers were published online about the novel disease @xcite , with each representing a potential faster end to a global pandemic and saved lives. despite the value of this quantity of focused research, it is infeasible " [...] the very deep convolutional networks are inspired by the "vggnet" architecture introduced in @xcite for the 2014 imagenet classification challenge, with the central idea to replace large convolutional kernels by small 3×3 kernels. salient? no ## citation-aware sciie dataset although citation network information has been shown to be effective in other tasks, few works have recently tried using it in sciie systems. one potential reason is the lack of a suitable dataset. thus, as a first contribution of this paper, we address this bottleneck by constructing a sciie dataset that is annotated with citation graph information. 4 specifically, we combine the rich annotations of scirex with a source of citation graph information, s2orc @xcite . for each paper, s2orc includes parsed metadata about which other papers cite this paper, which other papers are 0 1000 2000 in-degree 0 50 100 150 frequency 0 50 out-degree 0 20 40 figure 3: degree statistics of scirex documents in the citation graph. to merge scirex with s2orc, we link records using metadata obtained via the semantic scholar api: foot_2 paper title, doi string, arxiv id, and semantic scholar paper id. for each document in scirex, we check against all 81m documents in s2orc for exact matches on any of these identifiers, yielding s2orc entries for 433 out of 438 documents in scirex. the final mapping is included in our repository for the community to use. though our work only used the scirex dataset, our methods can be readily extended to other sciie datasets (including those mentioned in §2.1) using our released software. statistics examining the distribution of citations for all documents in the scirex dataset (in figure 3 ), we observe a long-tailed distribution of citations per paper, and a bell-shaped distribution of references per paper. in addition to the 5 documents we could not match to the s2orc citation graph, 7 were incorrectly recorded as containing no references and 5 others were incorrectly recorded as having no citations. these errors are due to data issues in the s2orc dataset, which relies on pdf parsers to extract information @xcite . ## citationie we now describe our citation-aware scientific ie architecture, which incorporates citation information into mention identification, salient entity classification, and relation extraction. for each task, we consider two types of citation graph information, either separately or together: (1) structural information from the graph network topology and (2) textual information from the content of citing and cited documents. ## implications and future directions we explore the use of citation graph information in neural scientific information extraction with cita-tionie, a model that can leverage either the structure of the citation graph or the content of citing or cited documents. we find that this information, combined with document text, leads to particularly strong improvements for salient entity classification and relation extraction, and provides an increase in end-to-end ie system performance over a strong baseline. our proposed methods reflect some of the simplest ways of incorporating citation graph information into a neural sciie system. as such, these results can be considered a proof of concept. in the future we will explore ways to extract richer information from the graph using more sophisticated techniques, hopefully better capturing the interplay between citation graph structure and content. finally, we evaluated our proof of concept here on a single dataset in the machine learning domain. while our methods are not domain-specific, verifying that these methods generalize to other scientific domains is important future work.