  Embedding based methods are widely used for unsupervised keyphrase extraction
(UKE) tasks. Generally, these methods simply calculate similarities between
phrase embeddings and document embedding, which is insufficient to capture
different context for a more effective UKE model. In this paper, we propose a
novel method for UKE, where local and global contexts are jointly modeled. From
a global view, we calculate the similarity between a certain phrase and the
whole 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 the
document where phrases are regarded as vertices and the edges are similarities
between vertices. Then, we proposed a new centrality computation method to
capture local salient information based on the graph structure. Finally, we
further combine the modeling of global and local context for ranking. We
evaluate our models on three public benchmarks (Inspec, DUC 2001, SemEval 2010)
and compare with existing state-of-the-art models. The results show that our
model outperforms most models while generalizing better on input documents with
different domains and length. Additional ablation study shows that both the
local and global information is crucial for unsupervised keyphrase extraction
tasks.
