Neural Text Simplification

Neural Text Simplification is a computational approach to text simplification that employs neural network architectures to automatically transform complex textual inputs into simpler, more accessible versions while preserving meaning.

34

Publications

1.8K

Citations

95

Authors

43

Institutions

Publications per year

2017–2026

25

Authors

95

  1. LS
    Lucia Specia

    University of Sheffield, United Kingdom

    4 publications · 283 citations

    4

    h-index

  2. SŠ
    Sanja Štajner

    University of Mannheim, Germany

    4 publications · 275 citations

    4

    h-index

  3. JQ
    Jipeng Qiang

    Yangzhou University, China

    3 publications · 94 citations

    3

    h-index

  4. MM
    Mounica Maddela

    Georgia Institute of Technology, United States

    2 publications · 182 citations

    2

    h-index

  5. DK
    David Kauchak

    Pomona College, United States

    2 publications · 143 citations

    2

    h-index

1–5 of 95

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 81 citations

    4

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 2 publications · 162 citations

    2

    h-index

  3. Computer Speech & Language

    1 publications · 34 citations

    1

    h-index

  4. Procedia Computer Science

    1 publications · 16 citations

    1

    h-index

  5. Language Resources and Evaluation

    1 publications · 14 citations

    1

    h-index

1–5