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Unsupervised Keyphrase Extraction with Multipartite Graphs

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arxiv 1803.08721 v2 pith:6GQRXVQS submitted 2018-03-23 cs.IR cs.CL

Florian Boudin This is my paper · ORCID
classification cs.IRcs.CL
keywords keyphrasemodelextractiongraphmultipartiteunsupervisedcandidatecandidates
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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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  1. ConExion: Concept Extraction with Large Language Models

    cs.CL 2025-04 conditional novelty 5.0 of 10

    Prompting Llama3 70B with one random in-context example achieves the best reported F1 on present-concept extraction for Inspec and SemEval2017, outperforming traditional unsupervised keyphrase extractors.

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