HG-News: News Headline Generation Based on a Generative Pre-Training Model
IEEE ACCESS(2021)
摘要
Neural headline generation models have recently shown great results since neural network methods have been applied to text summarization. In this paper, we focus on news headline generation. We propose a news headline generation model based on a generative pre-training model. In our model, we propose a rich features input module. The headline generation model we propose only contains a decoder incorporating the pointer mechanism and the n-gram language features, while other generation models use the encoder-decoder architecture. Experiments on news datasets show that our model achieves comparable results in the field of news headline generation.
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关键词
Mathematical model,Decoding,Task analysis,Vocabulary,Computational modeling,Neural networks,Convolution,Generation model,headline generation,text summarization,neural network
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