Summarization

Summarization is a data abstraction technique that distills essential information from a source document or dataset by preserving core meaning while reducing redundancy, distinguishing it from other abstraction methods through its focus on conciseness and representativeness.

4.2K

Publications

291.7K

Citations

9.1K

Authors

1.9K

Institutions

Publications per year

2017–2026

1.9K

Authors

9.1K

  1. DR
    Dragomir Radev

    University of Michigan, United States

    46 publications · 5.5K citations

    32

    h-index

  2. KM
    Kathleen McKeown

    Columbia University, United States

    47 publications · 4.7K citations

    31

    h-index

  3. FW
    Furu Wei

    Microsoft Research Asia (China), China

    34 publications · 3.1K citations

    26

    h-index

  4. WL
    Wenjie Li

    Hong Kong Polytechnic University, Hong Kong SAR China

    36 publications · 2.4K citations

    26

    h-index

  5. XW
    Xiaojun Wan

    Peking University, China

    35 publications · 2.4K citations

    24

    h-index

1–5 of 9.1K

Venues

  1. arXiv (Cornell University)

    Repository · 257 publications · 16.8K citations

    60

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 72 publications · 4.4K citations

    36

    h-index

  3. Information Processing & Management

    50 publications · 5K citations

    33

    h-index

  4. Expert Systems with Applications

    40 publications · 2.8K citations

    31

    h-index

  5. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 30 publications · 1.6K citations

    25

    h-index

1–5