Data-to-text Generation

Data-To-Text Generation is a subfield of natural language generation that specializes in transforming structured data into coherent and contextually appropriate textual narratives.

501

Publications

27.7K

Citations

1.4K

Authors

445

Institutions

Publications per year

2017–2026

358

Authors

1.4K

  1. ER
    Ehud Reiter

    University of Aberdeen, United Kingdom

    17 publications · 2.7K citations

    15

    h-index

  2. CM
    Chris Mellish

    University of Aberdeen, United Kingdom

    10 publications · 373 citations

    10

    h-index

  3. CG
    Claire Gardent

    Laboratoire Lorrain de Recherche en Informatique et ses Applications, France

    10 publications · 1K citations

    10

    h-index

  4. EK
    Emiel Krahmer

    Tilburg University, Netherlands

    8 publications · 1.2K citations

    8

    h-index

  5. OD
    Ondřej Dušek

    Charles University, Czechia

    6 publications · 143 citations

    7

    h-index

1–5 of 1.4K

Venues

  1. arXiv (Cornell University)

    Repository · 63 publications · 2.8K citations

    26

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 11 publications · 781 citations

    12

    h-index

  3. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 6 publications · 551 citations

    6

    h-index

  4. Natural Language Engineering

    6 publications · 920 citations

    6

    h-index

  5. 5

    h-index

1–5