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.
Unsupervised Keyphrase Extraction with Multipartite Graphs
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abstract
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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ConExion: Concept Extraction with Large Language Models
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.