  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.
