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EMNLP
2009

Clustering to Find Exemplar Terms for Keyphrase Extraction

10 years 18 days ago
Clustering to Find Exemplar Terms for Keyphrase Extraction
Keyphrases are widely used as a brief summary of documents. Since manual assignment is time-consuming, various unsupervised ranking methods based on importance scores are proposed for keyphrase extraction. In practice, the keyphrases of a document should not only be statistically important in the document, but also have a good coverage of the document. Based on this observation, we propose an unsupervised method for keyphrase extraction. Firstly, the method finds exemplar terms by leveraging clustering techniques, which guarantees the document to be semantically covered by these exemplar terms. Then the keyphrases are extracted from the document using the exemplar terms. Our method outperforms sate-of-the-art graphbased ranking methods (TextRank) by 9.5% in F1-measure.
Zhiyuan Liu, Peng Li, Yabin Zheng, Maosong Sun
Added 17 Feb 2011
Updated 17 Feb 2011
Type Journal
Year 2009
Where EMNLP
Authors Zhiyuan Liu, Peng Li, Yabin Zheng, Maosong Sun
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