  Keyphrase extraction (KE) aims to summarize a set of phrases that accurately
express a concept or a topic covered in a given document. Recently,
Sequence-to-Sequence (Seq2Seq) based generative framework is widely used in KE
task, and it has obtained competitive performance on various benchmarks. The
main challenges of Seq2Seq methods lie in acquiring informative latent document
representation and better modeling the compositionality of the target
keyphrases set, which will directly affect the quality of generated keyphrases.
In this paper, we propose to adopt the Dynamic Graph Convolutional Networks
(DGCN) to solve the above two problems simultaneously. Concretely, we explore
to integrate dependency trees with GCN for latent representation learning.
Moreover, the graph structure in our model is dynamically modified during the
learning process according to the generated keyphrases. To this end, our
approach is able to explicitly learn the relations within the keyphrases
collection and guarantee the information interchange between encoder and
decoder in both directions. Extensive experiments on various KE benchmark
datasets demonstrate the effectiveness of our approach.
