@article{
 title = {Clustering to Find Exemplar Terms for Keyphrase Extraction},
 type = {article},
 year = {2009},
 identifiers = {[object Object]},
 pages = {257-266},
 websites = {http://www.aclweb.org/anthology/D/D09/D09-1027},
 publisher = {Association for Computational Linguistics},
 id = {a7809367-aac6-3729-b998-d1545152af91},
 created = {2011-02-27T18:33:21.000Z},
 file_attached = {false},
 profile_id = {5284e6aa-156c-3ce5-bc0e-b80cf09f3ef6},
 group_id = {066b42c8-f712-3fc3-abb2-225c158d2704},
 last_modified = {2017-03-14T14:36:19.698Z},
 tags = {keyphrase extraction},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Liu2009},
 private_publication = {false},
 abstract = {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 graph-based ranking methods (TextRank) by 9.5% in F1-measure.},
 bibtype = {article},
 author = {Liu, Zhiyuan and Li, Peng and Zheng, Yabin and Sun, Maosong},
 journal = {Language}
}

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