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.

• Type: Journal
• Year: 2009
• Where: EMNLP
• Authors: Zhiyuan Liu, Peng Li, Yabin Zheng, Maosong Sun
• Keywords: Document, EMNLP 2009, Exemplar Terms, Keyphrase Extraction, Natural Language Processing