Sampling For Summarization

Sampling for Summarization is a data selection methodology that distinguishes itself by identifying and extracting a representative subset of data points from a larger dataset to facilitate efficient and accurate summarization, preserving critical information while reducing computational complexity.

21

Publications

1.9K

Citations

45

Authors

22

Institutions

Publications per year

2017–2026

4

Authors

45

  1. EC
    Edith Cohen

    AT&T (United States), United States

    4 publications · 146 citations

    4

    h-index

  2. DS
    Damian Straszak

    École Polytechnique Fédérale de Lausanne, Switzerland

    2 publications · 62 citations

    2

    h-index

  3. NK
    Nisheeth K. Vishnoi

    Yale University, United States

    2 publications · 62 citations

    2

    h-index

  4. PB
    Phillip B. Gibbons

    Carnegie Mellon University, United States

    2 publications · 526 citations

    2

    h-index

  5. BG
    Boris Glavic

    Illinois Institute of Technology, United States

    2 publications · 32 citations

    2

    h-index

1–5 of 45

Venues

  1. Proceedings of the VLDB Endowment

    PVLDB · 6 publications · 235 citations

    6

    h-index

  2. ACM SIGMOD Record

    3 publications · 392 citations

    3

    h-index

  3. 1

    h-index

  4. Data & Knowledge Engineering

    DKE · 1 publications · 26 citations

    1

    h-index

  5. International Conference on Machine Learning

    Conference · 1 publications · 32 citations

    1

    h-index

1–5