Abstract
Product reviews play an influential role for the e-commerce websites, as consumers leverage them during the purchase decision process. However, the volume of such reviews can be overwhelming for a web user to comprehend the gist of overall information communicated by other consumers. In this paper, we address the problem of summarizing user contributed product reviews, having certain properties that differentiate them significantly from summarizing of traditional text articles. We propose suitable summarization algorithms that capture useful information with minimum redundancy and maximum information. We present a graph based formulation using a fast and scalable greedy algorithm for the review summarization problem. Our approach provides a rich model that makes certain sentences more rewarding based on their properties, in addition to their relation to the other reviews. We evaluate and show that our proposed algorithm outperforms other state-of-the-art summarization algorithms with significance level of 0.01 using automatic evaluation.
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Modani, N., Khabiri, E., Srinivasan, H., Caverlee, J. (2015). Creating Diverse Product Review Summaries: A Graph Approach. In: Wang, J., et al. Web Information Systems Engineering – WISE 2015. WISE 2015. Lecture Notes in Computer Science(), vol 9418. Springer, Cham. https://doi.org/10.1007/978-3-319-26190-4_12
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DOI: https://doi.org/10.1007/978-3-319-26190-4_12
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