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

Authors

3.6K

  1. RD
    Robert Dale

    Macquarie University, Australia

    23 publications · 2.1K citations

    20

    h-index

  2. ER
    Ehud Reiter

    University of Aberdeen, United Kingdom

    25 publications · 3.3K citations

    18

    h-index

  3. KM
    Kathleen McKeown

    Columbia University, United States

    19 publications · 1.1K citations

    17

    h-index

  4. ML
    Mirella Lapata

    University of Edinburgh, United Kingdom

    16 publications · 1.7K citations

    16

    h-index

  5. CM
    Chris Mellish

    University of Aberdeen, United Kingdom

    20 publications · 715 citations

    15

    h-index

1–5 of 3.6K

Venues

  1. arXiv (Cornell University)

    Repository · 215 publications · 12.5K citations

    55

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 30 publications · 1.5K citations

    20

    h-index

  3. Computational Linguistics

    13 publications · 1.2K citations

    13

    h-index

  4. Natural Language Engineering

    11 publications · 1.2K citations

    11

    h-index

  5. 11

    h-index

1–5