Advances in Automatic Text Summarization
Advances in Automatic Text Summarization
Mining product reputations on the Web
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining the peanut gallery: opinion extraction and semantic classification of product reviews
WWW '03 Proceedings of the 12th international conference on World Wide Web
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Mining opinion features in customer reviews
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
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Product reviews written by on-line shoppers is a valuable source of information for potential new customers who desire to make an informed purchase decision. Manually processing quite a few dozens, or even hundreds, of reviews for a single product is tedious and time consuming. Although there exist mature and generic text summarization techniques, they are focused primarily on article type content and do not perform well on short and usually repetitive snippets of text found at on-line shops. In this paper, we propose MOpiS, a multiple opinion summarization algorithm that generates improved summaries of product reviews by taking into consideration metadata information that usually accompanies the on-line review text. We demonstrate the effectiveness of our approach with experimental results.