Answer Generation

Answer Generation is the computational process within question answering systems whereby a system produces a natural language response to a given query, distinguished from related processes by its focus on synthesizing linguistically coherent and contextually accurate answers rather than retrieving or selecting pre-existing content.

611

Publications

34.6K

Citations

1.9K

Authors

478

Institutions

Publications per year

2017–2026

371

Authors

1.9K

  1. SM
    Sanda M. Harabagiu

    The University of Texas at Dallas, United States

    12 publications · 1.6K citations

    12

    h-index

  2. EN
    Eric Nyberg

    Carnegie Mellon University, United States

    10 publications · 275 citations

    10

    h-index

  3. CX
    Caiming Xiong

    Salesforce (United States), United States

    10 publications · 396 citations

    10

    h-index

  4. DM
    Dan Moldovan

    University of Southern California, United States

    9 publications · 1.2K citations

    9

    h-index

  5. FW
    Furu Wei

    Microsoft Research Asia (China), China

    9 publications · 461 citations

    9

    h-index

1–5 of 1.9K

Venues

  1. arXiv (Cornell University)

    Repository · 85 publications · 4.8K citations

    33

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 18 publications · 847 citations

    16

    h-index

  3. IEEE Access

    10 publications · 307 citations

    10

    h-index

  4. 8

    h-index

  5. 5

    h-index

1–5