Document Summarization for Answering Non-Factoid Queries

We formulate a document summarization method to extract passage-level answers for non-factoid queries, referred to as answer-biased summaries. We propose to use external information from related Community Question Answering (CQA) content to better identify answer bearing sentences. Three optimizatio...

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Veröffentlicht in:	IEEE transactions on knowledge and data engineering 2018-01, Vol.30 (1), p.15-28
Hauptverfasser:	Yulianti, Evi, Ruey-Cheng Chen, Scholer, Falk, Croft, W. Bruce, Sanderson, Mark
Format:	Artikel
Sprache:	eng
Schlagworte:	answer-biased summaries CQA Data mining Document summarization Feature extraction Google Identification methods Knowledge discovery learning-to-rank non-factoid queries Optimization Queries Search engines Sentences State of the art Summaries Web search
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