Summarizing Online Reviews Using Aspect Rating Distributions and Language Modeling

Author

Di Fabbrizio, G. ; Aker, A. ; Gaizauskas, Robert

Author_Institution

Univ. of Sheffield, Sheffield, UK

Volume

28

Issue

3

fYear

2013

fDate

May-June 2013

Firstpage

28

Lastpage

37

Abstract

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.

Keywords

Internet; information retrieval; reviews; text analysis; Starlet; aspect rating distributions; information selection; language modeling; multidocument summarization extraction; online reviews summarization; opinion distribution; product review; sentences; service reviews; Computational linguistics; Computational modeling; Data mining; Feature extraction; Natural language processing; Predictive models; Text analysis; Computational linguistics; Computational modeling; Data mining; Feature extraction; Natural language processing; Predictive models; Text analysis; reviews summarization; rating prediction models; A* search;

fLanguage

English

Journal_Title

Intelligent Systems, IEEE

Publisher

ieee

ISSN

1541-1672

Type

jour

DOI

10.1109/MIS.2013.36

Filename

6497033