Paper: Answer Extraction Semantic Clustering And Extractive Summarization For Clinical Question Answering

ACL ID P06-1106
Title Answer Extraction Semantic Clustering And Extractive Summarization For Clinical Question Answering
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2006
Authors

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.