Extractive Summarization: Limits, Compression, Generalized Model and Heuristics

@article{Verma2017ExtractiveSL,
  title={Extractive Summarization: Limits, Compression, Generalized Model and Heuristics},
  author={Rakesh M. Verma and Daniel Lee},
  journal={ArXiv},
  year={2017},
  volume={abs/1704.05550}
}
  • Rakesh M. Verma, Daniel Lee
  • Published in Computación y Sistemas 2017
  • Computer Science
  • Due to its promise to alleviate information overload, text summarization has attracted the attention of many researchers. However, it has remained a serious challenge. Here, we first prove empirical limits on the recall (and F1-scores) of extractive summarizers on the DUC datasets under ROUGE evaluation for both the single-document and multi-document summarization tasks. Next we define the concept of compressibility of a document and present a new model of summarization, which generalizes… CONTINUE READING

    Create an AI-powered research feed to stay up to date with new papers like this posted to ArXiv

    7
    Twitter Mentions

    Citations

    Publications citing this paper.

    Collection-Document Summaries

    VIEW 2 EXCERPTS
    CITES BACKGROUND & METHODS

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 35 REFERENCES

    A study of global inference algorithms in multi-document summarization

    • R. McDonald
    • Proc. of the 29th ECIR. Springer
    • 2007
    VIEW 4 EXCERPTS
    HIGHLY INFLUENTIAL

    Recent automatic text summarization techniques: a survey

    VIEW 1 EXCERPT