Graph-to-text Generation

Graph-to-Text Generation is a natural language generation task that involves converting structured graph representations of data into coherent and contextually accurate natural language texts, distinguished by its focus on capturing relational and topological information encoded in graph structures.

198

Publications

10.7K

Citations

623

Authors

208

Institutions

Publications per year

2017–2026

151

Authors

623

  1. LW
    Lingfei Wu

    William & Mary, United States

    16 publications · 952 citations

    13

    h-index

  2. LS
    Linfeng Song

    University of Rochester, United States

    8 publications · 494 citations

    8

    h-index

  3. YZ
    Yue Zhang

    Westlake University, China

    6 publications · 399 citations

    6

    h-index

  4. ZW
    Zhiguo Wang

    IBM (United States), United States

    6 publications · 510 citations

    6

    h-index

  5. MJ
    Mohammed J. Zaki

    Rensselaer Polytechnic Institute, United States

    5 publications · 234 citations

    5

    h-index

1–5 of 623

Venues

  1. arXiv (Cornell University)

    Repository · 31 publications · 1.2K citations

    20

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 4 publications · 592 citations

    4

    h-index

  3. 3

    h-index

  4. ACM Transactions on Information Systems

    2 publications · 70 citations

    2

    h-index

  5. Journal of the Brazilian Computer Society

    JBCS · 2 publications · 240 citations

    2

    h-index

1–5