Text Generation

Text Generation is the computational process within Natural Language Processing whereby a system produces coherent and contextually appropriate natural language text from structured or unstructured input, distinguished from related linguistic modeling tasks by its primary focus on the automated creation of human-readable textual output.

7.1K

Publications

491.1K

Citations

15.6K

Authors

2.3K

Institutions

Publications per year

2017–2026

4.8K

Authors

15.6K

  1. YC
    Yejin Choi

    University of Washington, United States

    51 publications · 6.4K citations

    33

    h-index

  2. JG
    Jianfeng Gao

    Microsoft (United States), United States

    44 publications · 8.4K citations

    32

    h-index

  3. ER
    Ehud Reiter

    University of Aberdeen, United Kingdom

    52 publications · 5.4K citations

    29

    h-index

  4. KM
    Kathleen McKeown

    Columbia University, United States

    52 publications · 4K citations

    28

    h-index

  5. AM
    Alexander M. Rush

    Harvard University Press, United States

    32 publications · 5K citations

    27

    h-index

1–5 of 15.6K

Venues

  1. arXiv (Cornell University)

    Repository · 1.1K publications · 78.8K citations

    114

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 190 publications · 12.3K citations

    57

    h-index

  3. Computational Linguistics

    40 publications · 5K citations

    34

    h-index

  4. Transactions of the Association for Computational Linguistics

    TACL · 36 publications · 3.3K citations

    28

    h-index

  5. 27

    h-index

1–5