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| Content Provider | ACM Digital Library |
|---|---|
| Author | Xu, Xueke Meng, Tao Cheng, Xueqi |
| Abstract | In this paper, we study the aspect-based extractive summarization based on the observations that a good summary should present representative opinions on user concerned sub-aspects within limited words. According to these observations, we argue that, two requirements, i.e. representativeness and diversity, should be considered for generating a good summary in addition to the traditional requirements of aspect-relevance and sentiment intensity. We focus on the intrinsic relationship between sentences and the dependency between extracted sentences for summarization, and thus propose a novel aspect-based summarization method for online reviews, which employs an Aspect-sensitive Markov Random Walk Model to meet the representativeness requirement, as well as a greedy redundancy removal method to meet the diversity requirement. The conducted experiments verify the effectiveness of the proposed method by comparing it with the baselines which ignores representativeness and/or diversity. The experimental results also show that, the two requirements we present are both indispensable for a good summary. |
| Starting Page | 968 |
| Ending Page | 975 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781450301138 |
| DOI | 10.1145/1982185.1982396 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2011-03-21 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Representativeness Diversity Aspect-based summarization Review mining |
| Content Type | Text |
| Resource Type | Article |
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