title: Using outcome polarity in sentence extraction for medical question-answering.
abstract: Multiple pieces of text describing various pieces of evidence in clinical trials are often needed in answering a clinical question. We explore a multi-document summarization approach to automatically find this information for questions about effects of using a medication to treat a disease. Sentences in relevant documents are ranked according to various features by a machine learning approach. Those with higher scores are more important and will be included in the summary. The presence of clinical outcomes and their polarity are incorporated into the approach as features for determining importance of sentences, and the effectiveness of this is investigated, along with that of other textual features. The results show that information on clinical outcomes improves the performance of summarization.
authors.affiliation: Department of Computer Science, University of Toronto, Toronto, Ontario, Canada M5S 3G4.
authors.forename: Yun
authors.initials: Y
authors.lastname: Niu
authors.forename: Xiaodan
authors.initials: X
authors.lastname: Zhu
authors.forename: Graeme
authors.initials: G
authors.lastname: Hirst
mesh_terms: D004358:Drug Therapy; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D008037:Linguistics; D016239:MEDLINE; D009323:Natural Language Processing