Matching Citation Text and Cited Spans in Biomedical Literature: a Search-Oriented Approach

Human Language Technologies: The 2015 Annual Conference of the North American Chapter of the ACL, pages 1042–1048, Denver, Colorado, May 31 – June 5, 2015. c⃝2015 Association for Computational Linguistics Matching Citation Text and Cited Spans in Biomedical Literature: a Search-Oriented Approach Arman Cohan, Luca Soldaini, Nazli Goharian Georgetown University, Information Retrieval Lab, Computer Science Department {arman, luca, nazli}@ir.cs.georgetown.edu Abstract Citation sentences (citances) to a reference ar- ticle have been extensively studied for sum- marization tasks. However, citances might not accurately represent the content of the cited article, as they often fail to capture the con- text of the reported findings and can be af- fected by epistemic value drift. Following the intuition behind the TAC (Text Analysis Conference) 2014 Biomedical Summarization track, we propose a system that identifies text spans in the reference article that are related to a given citance. We refer to this problem as citance-reference spans matching. We ap- proach the problem as a retrieval task; in this paper, we detail a comparison of different ci- tance reformulation methods and their combi- nations. While our results show improvement over the baseline (up to 25.9%), their absolute magnitude implies that there is ample room for future improvement. 1 Introduction The size of scientific literature has increased dra- matically during recent decades. In biomedical do- main for example, PubMed – the largest repository of biomedical literature – contains more than 24 mil- lion articles. Thus, there is a need for concise pre- sentation of important findings in the scientific arti- cles being published. Text summarization of scien- tific articles is a method for such presentation. One obvious form of scientific summaries, is the abstract of the articles. Another type of scientific summaries relates to citance-based summaries which are sum- maries created using the set of citations to a refer- ence article. This kind of summary covers some aspects of the reference article which might not be present in its abstract (Elkiss et al., 2008). Citances often cover important and novel insights about findings or aspects of a paper that others Reference Article (Voorhoeve et al., 2006): “These miRNAs neutralize p53-mediated CDK inhibition, possibly through direct inhibition of the expression of the tumor suppressor LATS2.” Citing Article (Okada et al., 2011): “Two oncogenic miRNAs, miR-372 and miR- 373, directly inhibit the expression of Lats2, thereby allowing tu- morigenic growth in the presence of p53 (Voorhoeve et al., 2006).” Figure 1: Example of epistemic value drift from (De Waard and Maat, 2012). The claim in (Voorhoeve et al., 2006) becomes fact in (Okada et al., 2011). have found interesting; thus, they capture contribu- tions that had an impact on the research community (Elkiss et al., 2008; Qazvinian and Radev, 2008). In the past, many have focused on citance extrac- tion and citance-based summarization. Example of citance extraction include (Siddharthan and Teufel, 2007), who used a machine learning approach with linguistic, lexical, statistical and positional features, and (Kaplan et al., 2009), who studied a coreference resolution based approach. Citance extraction has …

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