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Discover Computing
Published in: Discover Computing 6/2017

24-07-2017

Product review summarization through question retrieval and diversification

Authors: Mengwen Liu, Yi Fang, Alexander G. Choulos, Dae Hoon Park, Xiaohua Hu

Published in: Discover Computing | Issue 6/2017

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Abstract

Product reviews have become an important resource for customers before they make purchase decisions. However, the abundance of reviews makes it difficult for customers to digest them and make informed choices. In our study, we aim to help customers who want to quickly capture the main idea of a lengthy product review before they read the details. In contrast with existing work on review analysis and document summarization, we aim to retrieve a set of real-world user questions to summarize a review. In this way, users would know what questions a given review can address and they may further read the review only if they have similar questions about the product. Specifically, we design a two-stage approach which consists of question selection and question diversification. For question selection phase, we first employ probabilistic retrieval models to locate candidate questions that are relevant to a given review. A Recurrent Neural Network Encoder–Decoder is utilized to measure the “answerability” of questions to a review. We then design a set function to re-rank the questions with the goal of rewarding diversity in the final question set. The set function satisfies submodularity and monotonicity, which results in an efficient greedy algorithm of submodular optimization. Evaluation on product reviews from two categories shows that the proposed approach is effective for discovering meaningful questions that are representative of individual reviews.
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Metadata
Title
Product review summarization through question retrieval and diversification
Authors
Mengwen Liu
Yi Fang
Alexander G. Choulos
Dae Hoon Park
Xiaohua Hu
Publication date
24-07-2017
Publisher
Springer Netherlands
Published in
Discover Computing / Issue 6/2017
Print ISSN: 2948-2984
Electronic ISSN: 2948-2992
DOI
https://doi.org/10.1007/s10791-017-9311-0

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