Keyphrase Extraction with Dynamic Graph Convolutional Networks and Diversified Inference

TL;DR

This paper introduces a novel keyphrase extraction method using Dynamic Graph Convolutional Networks that dynamically adapt during learning to improve the quality of generated keyphrases by better modeling document structure and relations.

Contribution

The paper proposes integrating dependency trees with DGCN and dynamically modifying the graph structure during training for enhanced keyphrase extraction.

Findings

Outperforms existing methods on multiple KE benchmarks.

Effectively models relations within keyphrases collection.

Improves latent document representation quality.

Abstract

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.