Unsupervised Keyphrase Extraction with Multipartite Graphs

Understand

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.