Summarizing online customer reviews automatically based on topical structure

Zhan, Jiaming, Loh, Han Tong and Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940 2008. Summarizing online customer reviews automatically based on topical structure. Presented at: Web Information Systems and Technologies 3rd International Conference (WEBIST 2007), Barcelona, Spain, 3-6 March 2007. Published in: Filipe, Joaquim and Cordeiro, José eds. Web Information Systems and Technologies: Third International Conference, WEBIST 2007, Barcelona, Spain, March 3-6, 2007, Revised Selected Papers. Lecture Notes in Business Information Processing (8) Berlin: Springer, pp. 245-256. 10.1007/978-3-540-68262-2_18

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Abstract

Online customer reviews offer valuable information for merchants and potential shoppers in e-Commerce and e-Business. However, even for a single product, the number of reviews often amounts to hundreds or thousands. Thus, summarization of multiple customer reviews is helpful to extract the important issues that merchants and customers are concerned with. Existing methods of multi-document summarization divide documents into non-overlapping clusters first and then summarize each cluster of documents individually with the assumption that each cluster discusses a single topic. When applied to summarize customer reviews, it is however difficult to determine the number of clusters a priori without the domain knowledge, and moreover, topics often overlap with each other in a collection of customer reviews. This paper proposes a summarization approach based on the topical structure of multiple customer reviews. Instead of clustering and summarization, our approach extracts topics from a collection of reviews and further ranks the topics based on their frequency. The summary is then generated according to the ranked topics. The evaluation results showed that our approach outperformed the baseline summarization systems, i.e. Copernic summarizer and clustering-summarization, in terms of users’ responsiveness.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Centre for Advanced Manufacturing Systems At Cardiff (CAMSAC) Engineering
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Uncontrolled Keywords:	customer reviews, multi-document summarization, topical structure, web mining
Publisher:	Springer
ISBN:	9783540682578
ISSN:	1865-1348
Related URLs:	Publisher
Last Modified:	25 Oct 2022 08:03
URI:	https://orca.cardiff.ac.uk/id/eprint/51238

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