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Product review summarization through question retrieval and diversification
Journal article   Peer reviewed

Product review summarization through question retrieval and diversification

Mengwen Liu, Yi Fang, Alexander Choulos, Dae Park and Xiaohua Hu
Information retrieval (Boston), v 20(6), pp 575-605
Dec 2017
DOI: https://doi.org/10.1007/s10791-017-9311-0
Featured in Collection :   UN Sustainable Development Goals @ Drexel

Abstract

Data Structures, Cryptology and Information Theory Pattern Recognition Computer Science Information Storage and Retrieval Data Mining and Knowledge Discovery Review summarization Diversity Document Preparation and Text Processing Question retrieval Sequence-to-sequence learning
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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9 Record Views
18 citations in Web of Science
22 citations in Scopus

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UN Sustainable Development Goals (SDGs)

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#4 Quality Education

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Collaboration types
Industry collaboration
Domestic collaboration
Web of Science research areas
Computer Science, Information Systems
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