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Summarizing Online Reviews Using Aspect Rating Distributions and Language Modeling
May-June 2013 (vol. 28 no. 3)
pp. 28-37
Giuseppe Di Fabbrizio, University of Sheffield
Ahmet Aker, University of Sheffield
Robert Gaizauskas, University of Sheffield
Product and service reviews are abundantly available online, but selecting relevant information from them involves a significant amount of time. The authors address this problem with Starlet, a novel approach for extracting multidocument summarizations that considers aspect rating distributions and language modeling. These features encourage the inclusion of sentences in the summary that preserve the overall opinion distribution and reflect the reviews' original language.
Index Terms:
Predictive models,Feature extraction,Computational linguistics,Computational modeling,Data mining,Natural language processing,Text analysis,reviews summarization; rating prediction models; A* search,Predictive models,Feature extraction,Computational linguistics,Computational modeling,Data mining,Natural language processing,Text analysis
Citation:
Giuseppe Di Fabbrizio, Ahmet Aker, Robert Gaizauskas, "Summarizing Online Reviews Using Aspect Rating Distributions and Language Modeling," IEEE Intelligent Systems, vol. 28, no. 3, pp. 28-37, May-June 2013, doi:10.1109/MIS.2013.36
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