Name
Abstract | ||
|---|---|---|
In an active e-commerce environment, customers process a large number of reviews when deciding on whether to buy a product or not. Abstractive Multi-Review Summarization aims to assist users to efficiently consume the reviews that are the most relevant to them. We propose the first large-scale abstractive multi-review summarization dataset that leverages more than 17.9 billion raw reviews and uses novel aspect-alignment techniques based on aspect annotations. Furthermore, we demonstrate that one can generate higher-quality review summaries by using a novel aspect-alignment-based model. Results from both automatic and human evaluation show that the proposed dataset plus the innovative aspect-alignment model can generate high-quality and trustful review summaries.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3397271.3401439 | SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval
Virtual Event
China
July, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-8016-4 | 0 |
PageRank | References | Authors |
0.34 | 1 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haojie Pan | 1 | 8 | 3.23 |
| Rongqin Yang | 2 | 5 | 1.11 |
| Xin Zhou | 3 | 126 | 15.50 |
| Rui Wang | 4 | 0 | 0.34 |
| Deng Cai | 5 | 7938 | 320.26 |
| Xiaozhong Liu | 6 | 368 | 48.27 |