Clustering to Find Exemplar Terms for Keyphrase Extraction

By Zhiyuan Liu, Peng Li, Yabin Zheng, Maosong Sun. Conference on Empirical Methods in Natural Language Processing, 2009

356 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/f280c160c1c749727b905b56364bb2d764b35629

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In Artificial Intelligence

Citations in the corpus

PositionRank: An Unsupervised Approach to Keyphrase Extraction from Scholarly Documents (2017, methodology): “Several unsupervised approaches leverage word clustering techniques such as first grouping candidate words into topics and then, extracting one representative keyphrase from each topic (Liu et al., 2009; Bougouin et al., 2013).”
SemEval-2010 Task 5 : Automatic Keyphrase Extraction from Scientific Articles (2010, methodology): “Recently, a resurgence of interest in keyphrase extraction has led to the development of several new systems and techniques for the task (Frank et al., 1999; Witten et al., 1999; Turney, 1999; Hulth, 2003; Turney, 2003; Park et al., 2004; Barker and Corrnacchia, 2000; Hulth, 2004; Matsuo and Ishizuk”
Deep Keyphrase Generation (2017, background): “As for unsupervised approaches, primary ideas include ﬁnding the central nodes in text graph (Mihalcea and Tarau, 2004; Grineva et al., 2009), detecting representative phrases from topical clusters (Liu et al., 2009, 2010), and so on.”