Name
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 retrieval and question diversification. We first propose probabilistic retrieval models to locate candidate questions that are relevant 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 for individual reviews. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2911451.2911544 | SIGIR |
Keywords | Field | DocType |
Review summarization,Question retrieval,Diversification | Set function,Data mining,Information retrieval,Computer science,Submodular set function,Greedy algorithm,Review analysis,Diversification (marketing strategy),If and only if,Product reviews,Probabilistic logic | Conference |
Citations | PageRank | References |
3 | 0.39 | 29 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mengwen Liu | 1 | 42 | 5.99 |
| Yi Fang | 2 | 379 | 32.01 |
| Dae Hoon Park | 3 | 110 | 7.29 |
| Xiaohua Hu | 4 | 2819 | 314.15 |
| Zhengtao Yu | 5 | 460 | 69.08 |