Linked Life Data

17238411

Source:http://linkedlifedata.com/resource/pubmed/id/17238411

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2007-1-22
pubmed:abstractText
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.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1942-597X
pubmed:author
pubmed:issnType
Electronic
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
599-603
pubmed:dateRevised
2009-3-9
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Using outcome polarity in sentence extraction for medical question-answering.
pubmed:affiliation
Department of Computer Science, University of Toronto, Toronto, Ontario, Canada M5S 3G4.
pubmed:publicationType
Journal Article