HG-News: News Headline Generation Based on a Generative Pre-Training Model
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 propos...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9507422/ |
| _version_ | 1819157277718872064 |
|---|---|
| author | Ping Li Jiong Yu Jiaying Chen Binglei Guo |
| author_facet | Ping Li Jiong Yu Jiaying Chen Binglei Guo |
| author_sort | Ping Li |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T16:06:13Z |
| format | Article |
| id | doaj.art-069553e98974466292e99fcc3e07b923 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T16:06:13Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-069553e98974466292e99fcc3e07b9232022-12-21T18:20:36ZengIEEEIEEE Access2169-35362021-01-01911003911004610.1109/ACCESS.2021.31027419507422HG-News: News Headline Generation Based on a Generative Pre-Training ModelPing Li0https://orcid.org/0000-0001-7045-0945Jiong Yu1Jiaying Chen2Binglei Guo3College of Information Science and Engineering, Xinjiang University, Ürümqi, Xinjiang, ChinaCollege of Information Science and Engineering, Xinjiang University, Ürümqi, Xinjiang, ChinaCollege of Information Science and Engineering, Xinjiang University, Ürümqi, Xinjiang, ChinaSchool of Computer Engineering, Hubei University of Arts and Science, Xiangyang, ChinaNeural 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.https://ieeexplore.ieee.org/document/9507422/Generation modelheadline generationtext summarizationneural network |
| spellingShingle | Ping Li Jiong Yu Jiaying Chen Binglei Guo HG-News: News Headline Generation Based on a Generative Pre-Training Model IEEE Access Generation model headline generation text summarization neural network |
| title | HG-News: News Headline Generation Based on a Generative Pre-Training Model |
| title_full | HG-News: News Headline Generation Based on a Generative Pre-Training Model |
| title_fullStr | HG-News: News Headline Generation Based on a Generative Pre-Training Model |
| title_full_unstemmed | HG-News: News Headline Generation Based on a Generative Pre-Training Model |
| title_short | HG-News: News Headline Generation Based on a Generative Pre-Training Model |
| title_sort | hg news news headline generation based on a generative pre training model |
| topic | Generation model headline generation text summarization neural network |
| url | https://ieeexplore.ieee.org/document/9507422/ |
| work_keys_str_mv | AT pingli hgnewsnewsheadlinegenerationbasedonagenerativepretrainingmodel AT jiongyu hgnewsnewsheadlinegenerationbasedonagenerativepretrainingmodel AT jiayingchen hgnewsnewsheadlinegenerationbasedonagenerativepretrainingmodel AT bingleiguo hgnewsnewsheadlinegenerationbasedonagenerativepretrainingmodel |