HG-News: News Headline Generation Based on a Generative Pre-Training Model

被引：3

作者：

Li, Ping ^{[1
]}

Yu, Jiong ^{[1
]}

Chen, Jiaying ^{[1
]}

Guo, Binglei ^{[2
]}

机构：

[1] Xinjiang Univ, Coll Informat Sci & Engn, Urumqi 830000, Xinjiang, Peoples R China

[2] Hubei Univ Arts & Sci, Sch Comp Engn, Xiangyang 441053, Peoples R China

来源：

IEEE ACCESS | 2021年 / 9卷

基金：

中国国家自然科学基金;

关键词：

Mathematical model; Decoding; Task analysis; Vocabulary; Computational modeling; Neural networks; Convolution; Generation model; headline generation; text summarization; neural network;

D O I：

10.1109/ACCESS.2021.3102741

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

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.

引用

页码：110039 / 110046

页数：8