Multimodal Summarization

Multimodal Summarization is a computational framework that synthesizes cohesive and concise representations from aligned inputs across multiple modalities—such as text, audio, and visual data—by identifying and integrating salient information while preserving cross-modal semantics and hierarchical structure.

145

Publications

5.4K

Citations

477

Authors

177

Institutions

Publications per year

2017–2026

88

Authors

477

  1. JZ
    Junnan Zhu

    University of Chinese Academy of Sciences, China

    8 publications · 633 citations

    8

    h-index

  2. CZ
    Chengqing Zong

    Chinese Academy of Sciences, China

    8 publications · 633 citations

    8

    h-index

  3. JZ
    Jiajun Zhang

    University of Chinese Academy of Sciences, China

    7 publications · 617 citations

    7

    h-index

  4. HL
    Haoran Li

    University of Chinese Academy of Sciences, China

    7 publications · 653 citations

    7

    h-index

  5. AZ
    Athanasia Zlatintsi

    National Technical University of Athens, Greece

    5 publications · 432 citations

    5

    h-index

1–5 of 477

Venues

  1. IEEE Transactions on Multimedia

    8 publications · 548 citations

    8

    h-index

  2. arXiv (Cornell University)

    Repository · 7 publications · 137 citations

    7

    h-index

  3. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 5 publications · 280 citations

    5

    h-index

  4. Knowledge-Based Systems

    3 publications · 109 citations

    3

    h-index

  5. Multimedia Tools and Applications

    3 publications · 45 citations

    3

    h-index

1–5