Knowledge-to-text Generation

Knowledge-to-Text Generation is a natural language generation task that involves the automated production of coherent and contextually appropriate textual narratives from structured or semi-structured knowledge representations, distinguished by its focus on conveying factual information embedded in formal knowledge sources such as databases, ontologies, or knowledge graphs.

266

Publications

15.9K

Citations

873

Authors

275

Institutions

Publications per year

2017–2026

127

Authors

873

  1. UH
    Udo Hahn

    Friedrich Schiller University Jena, Germany

    6 publications · 325 citations

    6

    h-index

  2. CX
    Caiming Xiong

    Salesforce (United States), United States

    5 publications · 248 citations

    5

    h-index

  3. ER
    Ehud Reiter

    University of Aberdeen, United Kingdom

    5 publications · 239 citations

    5

    h-index

  4. IA
    Ion Androutsopoulos

    Athens University of Economics and Business, Greece

    5 publications · 150 citations

    5

    h-index

  5. XW
    Xindong Wu

    Hefei University of Technology, China

    4 publications · 1.3K citations

    4

    h-index

1–5 of 873

Venues

  1. arXiv (Cornell University)

    Repository · 31 publications · 899 citations

    17

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 8 publications · 476 citations

    7

    h-index

  3. ePrints Soton (University of Southampton)

    Repository · 4 publications · 160 citations

    4

    h-index

  4. Neurocomputing

    3 publications · 74 citations

    3

    h-index

  5. Knowledge-Based Systems

    3 publications · 109 citations

    3

    h-index

1–5