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| Content Provider | ACM Digital Library |
|---|---|
| Author | Bellaachia, Abdelghani Al-Dhelaan, Mohammed |
| Abstract | In a multi-document settings, graph-based extractive summarization approaches build a similarity graph out of sentences in each cluster of documents then use graph centrality approaches to measure the importance of sentences. The similarity is computed between each pair of sentences. However, it is not clear if such approach captures high-order relations among more than two sentences or can differentiate between descriptive sentences of the cluster in comparison with other clusters. In this paper, we propose to model sentences as hyperedges and words as vertices using a hypergraph and combine it with topic signatures to differentiate between descriptive sentences and non-descriptive sentences. To rank sentences, we propose a new random walk over hyperedges that will prefer descriptive sentences of the cluster when measuring their centrality scores. Our approach outperform a number of baseline in the DUC 2001 dataset using the ROUGE metric. |
| Starting Page | 1919 |
| Ending Page | 1922 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781450325981 |
| DOI | 10.1145/2661829.2662036 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2014-11-03 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Multi-document summarization Text hypergraph Text summarization Topic signatures |
| Content Type | Text |
| Resource Type | Article |
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