Paper: Clustering to Find Exemplar Terms for Keyphrase Extraction

ACL ID D09-1027
Title Clustering to Find Exemplar Terms for Keyphrase Extraction
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009

Keyphrases are widely used as a brief summary of documents. Since man- ual assignment is time-consuming, vari- ous 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 docu- ment, but also have a good coverage of the document. Based on this observa- tion, we propose an unsupervised method for keyphrase extraction. Firstly, the method finds exemplar terms by leverag- ing clustering techniques, which guaran- tees the document to be semantically cov- ered by these exemplar terms. Then the keyphrases are extracted from the doc- ument using the exemplar terms. Our method outperforms sate-of-the-art graph- based ranking methods (TextRank) by 9.5% in F1-measure.