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Unsupervised Keyphrase Extraction with Multipartite Graphs

Florian Boudin


We propose an unsupervised keyphrase extraction model that encodes topical information within a multipartite graph structure. Our model represents keyphrase candidates and topics in a single graph and exploits their mutually reinforcing relationship to improve candidate ranking. We further introduce a novel mechanism to incorporate keyphrase selection preferences into the model. Experiments conducted on three widely used datasets show significant improvements over state-of-the-art graph-based models.
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hal-01983546 , version 1 (16-01-2019)


  • HAL Id : hal-01983546 , version 1


Florian Boudin. Unsupervised Keyphrase Extraction with Multipartite Graphs. 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL HLT), Jun 2018, Nouvelle Orléans, United States. pp.667 - 672. ⟨hal-01983546⟩
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