Research Area:  Machine Learning

Abstract:

Keyphrase generation (KG) aims to generate a set of keyphrases given a document, which is a fundamental task in natural language processing (NLP). Most previous methods solve this problem in an extractive manner, while recently, several attempts are made under the generative setting using deep neural networks. However, the state-of-the-art generative methods simply treat the document title and the document main body equally, ignoring the leading role of the title to the overall document. To solve this problem, we introduce a new model called Title-Guided Network (TG-Net) for automatic keyphrase generation task based on the encoder-decoder architecture with two new features: (i) the title is additionally employed as a query-like input, and (ii) a title-guided encoder gathers the relevant information from the title to each word in the document. Experiments on a range of KG datasets demonstrate that our model outperforms the state-of-the-art models with a large margin, especially for documents with either very low or very high title length ratios.

Keywords:  
Title-Guided Network
Keyphrase Generation
natural language processing
deep neural networks
generative methods
Machine Learning
Deep Learning

Author(s) Name:  Wang Chen, Yifan Gao, Jiani Zhang, Irwin King, Michael R. Lyu

Publisher name:  arXiv:1808.08575

DOI:  10.48550/arXiv.1808.08575

Volume Information:  

Paper Link:   https://arxiv.org/abs/1808.08575