Keyphrase Extraction with Dynamic Graph Convolutional Networks and Diversified Inference
This work addresses keyphrase extraction for document summarization, presenting an incremental improvement over existing Seq2Seq methods.
The paper tackles the challenges of acquiring informative latent document representations and modeling keyphrase compositionality in keyphrase extraction by proposing Dynamic Graph Convolutional Networks (DGCN) that integrate dependency trees and dynamically modify graph structures during learning, achieving effectiveness as demonstrated in extensive experiments on various benchmarks.
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.