Answer Extraction, Semantic Clustering, and Extractive Summarization for Clinical Question Answering

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 841–848, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Answer Extraction, Semantic Clustering, and Extractive Summarization for Clinical Question Answering Dina Demner-Fushman1,3 and Jimmy Lin1,2,3 1Department of Computer Science 2College of Information Studies 3Institute for Advanced Computer Studies University of Maryland College Park, MD 20742, USA demner@cs.umd.edu, jimmylin@umd.edu Abstract This paper presents a hybrid approach to question answering in the clinical domain that combines techniques from summarization and information retrieval. We tackle a frequently-occurring class of questions that takes the form “What is the best drug treatment for X?” Starting from an initial set of MEDLINE citations, our system first identifies the drugs un- der study. Abstracts are then clustered us- ing semantic classes from the UMLS on- tology. Finally, a short extractive sum- mary is generated for each abstract to pop- ulate the clusters. Two evaluations—a manual one focused on short answers and an automatic one focused on the support- ing abstracts—demonstrate that our sys- tem compares favorably to PubMed, the search system most widely used by physi- cians today. 1 Introduction Complex information needs can rarely be ad- dressed by single documents, but rather require the integration of knowledge from multiple sources. This suggests that modern information retrieval systems, which excel at producing ranked lists of documents sorted by relevance, may not be suffi- cient to provide users with a good overview of the “information landscape”. Current question answering systems aspire to address this shortcoming by gathering relevant “facts” from multiple documents in response to information needs. The so-called “definition” or “other” questions at recent TREC evalua- tions (Voorhees, 2005) serve as good examples: “good answers” to these questions include inter- esting “nuggets” about a particular person, organi- zation, entity, or event. The importance of cross-document information synthesis has not escaped the attention of other re- searchers. The last few years have seen a conver- gence between the question answering and sum- marization communities (Amig´o et al., 2004), as highlighted by the shift from generic to query- focused summaries in the 2005 DUC evalua- tion (Dang, 2005). Despite a focus on document ranking, different techniques for organizing search results have been explored by information retrieval researchers, as exemplified by techniques based on clustering (Hearst and Pedersen, 1996; Dumais et al., 2001; Lawrie and Croft, 2003). Our work, which is situated in the domain of clinical medicine, lies at the intersection of ques- tion answering, information retrieval, and summa- rization. We employ answer extraction to identify short answers, semantic clustering to group sim- ilar results, and extractive summarization to pro- duce supporting evidence. This paper describes how each of these capabilities contributes to an in- formation system tailored to the requirements of physicians. Two separate evaluations demonstrate the effectiveness of our approach. 2 Clinical Information Needs Although the need to answer questions related to patient care has been well documented (Cov- ell et al., 1985; Gorman et al., 1994; Ely et al., …

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