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Issue No. 01 - Jan. (2018 vol. 30)
ISSN: 1041-4347
pp: 15-28
Evi Yulianti , RMIT University, Melbourne, VIC, Australia
Ruey-Cheng Chen , RMIT University, Melbourne, VIC, Australia
Falk Scholer , RMIT University, Melbourne, VIC, Australia
W. Bruce Croft , RMIT University, Melbourne, VIC, Australia
Mark Sanderson , RMIT University, Melbourne, VIC, Australia
ABSTRACT
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 optimization-based methods are proposed: (i) query-biased, (ii) CQA-answer-biased, and (iii) expanded-query-biased, where expansion terms were derived from related CQA content. A learning-to-rank-based method is also proposed that incorporates a feature extracted from related CQA content. Our results show that even if a CQA answer does not contain a perfect answer to a query, their content can be exploited to improve the extraction of answer-biased summaries from other corpora. The quality of CQA content is found to impact on the accuracy of optimization-based summaries, though medium quality answers enable the system to achieve a comparable (and in some cases superior) accuracy to state-of-the-art techniques. The learning-to-rank-based summaries, on the other hand, are not significantly influenced by CQA quality. We provide a recommendation of the best use of our proposed approaches in regard to the availability of different quality levels of related CQA content. As a further investigation, the reliability of our approaches was tested on another publicly available dataset.
INDEX TERMS
Knowledge discovery, Feature extraction, Data mining, Search engines, Optimization, Google, Web search
CITATION

E. Yulianti, R. Chen, F. Scholer, W. B. Croft and M. Sanderson, "Document Summarization for Answering Non-Factoid Queries," in IEEE Transactions on Knowledge & Data Engineering, vol. 30, no. 1, pp. 15-28, 2018.
doi:10.1109/TKDE.2017.2754373
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