TextSumEval aim to provide a platform for the researcher working the field of text summarization and evaluation. In this task, we have provided the system-generated summaries and gold summaries to the task participants. Participants need to design a summary evaluation technique that compares system-generated summaries with gold summaries and generated their semantic resemblance score. To facilitate the above-mentioned task, we have provided a total of 6000 summaries which are selected from the existing databases, namely, CNNDM, MSMO, and XSUM. We have calculated the correlation scores between the BLEU score, ROUGE score, and our baseline system’s semantic score with participants submitted semantic scores using a Pearson correlation coefficient to verify the participants’ scores. Moreover, based on the mean of the correlation scores, we have highlighted the methodology of the best-performed team.

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TextSumEval-2022: Overview of the Textual Summary Evaluation Task

  • Tamwo,
  • Pankaj Dadure,
  • Prottay Kumar Adhikary,
  • Partha Pakray,
  • Benoit Favre,
  • Sivaji Bandyopadhyay,
  • Thierry Artires

摘要

TextSumEval aim to provide a platform for the researcher working the field of text summarization and evaluation. In this task, we have provided the system-generated summaries and gold summaries to the task participants. Participants need to design a summary evaluation technique that compares system-generated summaries with gold summaries and generated their semantic resemblance score. To facilitate the above-mentioned task, we have provided a total of 6000 summaries which are selected from the existing databases, namely, CNNDM, MSMO, and XSUM. We have calculated the correlation scores between the BLEU score, ROUGE score, and our baseline system’s semantic score with participants submitted semantic scores using a Pearson correlation coefficient to verify the participants’ scores. Moreover, based on the mean of the correlation scores, we have highlighted the methodology of the best-performed team.