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Title: Summarization of Multiple, Metadata Rich, Product Reviews
Author(s): F. Kokkoras, E. Lampridou, K. Ntonas, I. Vlahavas.
Availability: Click here to download the PDF (Acrobat Reader) file.
Keywords: web mining, opinion mining, summarization, ahp.
Appeared in: Workshop on Mining Social Data (MSoDa), 18th European Conference on Artificial Intelligence (ECAI '08), Patras, Greece, 2008.
Abstract: Modern successful on-line shops and product comparison sites allow consumers to express their opinion on products and services they purchased. Although such information can be useful to other potential customers, reading and mentally processing quite a few dozens or even hundreds of reviews for a single product is tedious and time consuming. In this paper, we propose ReSum a novel summarization ap-proach for multiple, metadata augmented, product reviews. We argue that the contribution of additional information (metadata) such as the user's expertise, the usefulness of the review to other users, etc., is significant and can result in improved summaries. The summarization algorithm we propose outperforms two commercial, general purpose summarizers that ignore such metadata.
See also : DEiXTo

        This paper has been cited by the following:

1 Cyril Labbe, Francois Portet, "Towards an Abstractive Opinion Summarisation of Multiple Reviews in the Tourism Domain", in Proc. 1st International Workshop on Sentiment Discovery from Affective Data (SDAD 2012), M. Gaber, M. Cocea, S. Weibelzahl, E. Menasalvas, C. Labbe, (Eds.), CEUR Workshop Proceedings ISSN 1613-0073, pp.87-94, Bristol, UK, September 28, 2012.