Automated Text Generation
Automated Text Generation is a computational method that systematically produces coherent natural language text from structured data or algorithmic inputs through rule-based, statistical, or neural modeling techniques.
1.4K
Publications
87.3K
Citations
3.6K
Authors
864
Institutions
Publications per year
2017–2026
848
Parents
1.4K
fairseq: A Fast, Extensible Toolkit for Sequence Modeling
Myle Ott, Sergey Edunov, Alexei Baevski et al. · 2019 · 2.5K citations · Full text
Prefix-Tuning: Optimizing Continuous Prompts for Generation
Xiang Lisa Li, Percy Liang · 2021 · 2.2K citations · Full text
Building Natural-Language Generation Systems
Ehud Reiter · ArXiv.org · 1996 · 1.3K citations · Full text
Hierarchical Neural Story Generation
Angela Fan, Mike Lewis, Yann Dauphin · 2018 · 1.1K citations · Full text
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University of Edinburgh
Edinburgh, United Kingdom
29 h-index
95 publications
6.4K citations
Carnegie Mellon University
Pittsburgh, United States
27 h-index
121 publications
6.9K citations
Google (United States)
Mountain View, United States
25 h-index
154 publications
10.2K citations

Peking University
Beijing, China
25 h-index
119 publications
6.4K citations
Microsoft (United States)
Redmond, United States
25 h-index
93 publications
10.9K citations

University of Aberdeen
Aberdeen, United Kingdom
21 h-index
45 publications
4.5K citations
University of Washington
Seattle, United States
21 h-index
73 publications
10K citations
Columbia University
New York, United States
20 h-index
48 publications
3.1K citations
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Repository · 215 publications · 12.5K citations
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Conference · 30 publications · 1.5K citations
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11 publications · 1.2K citations
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Conference · 12 publications · 366 citations
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