Graph-based Summarization

Graph-Based Summarization is a text summarization approach that models the document as a graph structure, where sentences or words are represented as nodes and their semantic or contextual relationships as edges, to identify the most salient information through graph-theoretic measures such as centrality or connectivity.

343

Publications

19.8K

Citations

863

Authors

348

Institutions

Publications per year

2017–2026

136

Authors

863

  1. XW
    Xiaojun Wan

    Peking University, China

    12 publications · 1K citations

    12

    h-index

  2. FW
    Furu Wei

    Microsoft Research Asia (China), China

    7 publications · 260 citations

    7

    h-index

  3. FB
    Francesco Bonchi

    Yahoo (Spain), Spain

    6 publications · 206 citations

    6

    h-index

  4. DK
    Danai Koutra

    University of Michigan, United States

    6 publications · 359 citations

    6

    h-index

  5. LP
    Laura Plaza

    Universidad Nacional de Educación a Distancia, Spain

    6 publications · 189 citations

    6

    h-index

1–5 of 863

Venues

  1. arXiv (Cornell University)

    Repository · 13 publications · 758 citations

    11

    h-index

  2. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 7 publications · 276 citations

    7

    h-index

  3. Proceedings of the VLDB Endowment

    PVLDB · 5 publications · 167 citations

    5

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 5 publications · 191 citations

    5

    h-index

  5. Expert Systems with Applications

    5 publications · 259 citations

    5

    h-index

1–5