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From task to evaluation: an automatic text summarization review

  • Lingfeng Lu
  • , Yang Liu
  • , Weiqiang Xu
  • , Huakang Li
  • , Guozi Sun*
  • *Corresponding author for this work
    • Nanjing University of Posts and Telecommunications

    Research output: Contribution to journalArticlepeer-review

    5 Citations (Scopus)

    Abstract

    Automatic summarization is attracting increasing attention as one of the most promising research areas. This technology has been tried in various real-world applications in recent years and achieved a good response. However, the applicability of conventional evaluation metrics cannot keep up with rapidly evolving summarization task formats and ensuing indicator. After recent years of research, automatic summarization task requires not only readability and fluency, but also informativeness and consistency. Diversified application scenarios also bring new challenges both for generative language models and evaluation metrics. In this review, we analysis and specifically focus on the difference between the task format and the evaluation metrics.

    Original languageEnglish
    Pages (from-to)2477-2507
    Number of pages31
    JournalArtificial Intelligence Review
    Volume56
    DOIs
    Publication statusPublished - Nov 2023

    Keywords

    • Automatic text summarization
    • Natural language generates
    • Real-world application
    • Text summarization evaluation

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