Research Area:  Machine Learning
In this paper, we present a novel integrated approach for keyphrase generation (KG). Unlike previous works which are purely extractive or generative, we first propose a new multi-task learning framework that jointly learns an extractive model and a generative model. Besides extracting keyphrases, the output of the extractive model is also employed to rectify the copy probability distribution of the generative model, such that the generative model can better identify important contents from the given document. Moreover, we retrieve similar documents with the given document from training data and use their associated keyphrases as external knowledge for the generative model to produce more accurate keyphrases. For further exploiting the power of extraction and retrieval, we propose a neural-based merging module to combine and re-rank the predicted keyphrases from the enhanced generative model, the extractive model, and the retrieved keyphrases. Experiments on the five KG benchmarks demonstrate that our integrated approach outperforms the state-of-the-art methods.
Keywords:  
Keyphrase Generation
Retrieval And Extraction
Machine Learning
Deep Learning
Author(s) Name:   Wang Chen, Hou Pong Chan, Piji Li, Lidong Bing, Irwin King
Journal name:  Computer Science
Conferrence name:  
Publisher name:  arXiv:1904.03454
DOI:  10.48550/arXiv.1904.03454
Volume Information:  
Paper Link:   https://arxiv.org/abs/1904.03454