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Embedding based methods are widely used for unsupervised keyphrase extraction(UKE) tasks. Generally, these methods simply calculate similarities betweenphrase embeddings and document embedding, which is insufficient to capturedifferent context for a more effective UKE model. In this paper, we propose anovel method for UKE, where local and global contexts are jointly modeled. Froma global view, we calculate the similarity between a certain phrase and thewhole document in the vector space as transitional embedding based models do.In terms of the local view, we first build a graph structure based on thedocument where phrases are regarded as vertices and the edges are similaritiesbetween vertices. Then, we proposed a new centrality computation method tocapture local salient information based on the graph structure. Finally, wefurther combine the modeling of global and local context for ranking. Weevaluate our models on three public benchmarks (Inspec, DUC 2001, SemEval 2010)and compare with existing state-of-the-art models. The results show that ourmodel outperforms most models while generalizing better on input documents withdifferent domains and length. Additional ablation study shows that both thelocal and global information is crucial for unsupervised keyphrase extractiontasks.

Unsupervised Keyphrase Extraction by Jointly Modeling Local and Global Context