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

摘要

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.