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Báo cáo khoa học: "Answer Extraction, Semantic Clustering, and Extractive Summarization for Clinical Question Answering"

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 under study. Abstracts are then clustered using semantic classes from the UMLS ontology. Finally, a short extractive summary is generated for each abstract to populate the clusters. . | Answer Extraction Semantic Clustering and Extractive Summarization for Clinical Question Answering Dina Demner-Fushman1 3 and Jimmy Lin1 2 3 department 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 under study. Abstracts are then clustered using semantic classes from the UMLS ontology. Finally a short extractive summary is generated for each abstract to populate the clusters. Two evaluations a manual one focused on short answers and an automatic one focused on the supporting abstracts demonstrate that our system compares favorably to PubMed the search system most widely used by physicians today. 1 Introduction Complex information needs can rarely be addressed 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 sufficient 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 evaluations Voorhees 2005 serve as good examples good answers to these questions include interesting nuggets about a particular person organization entity or event. The importance of cross-document information synthesis has not escaped the attention of other researchers. The last few years have seen a convergence