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Neural summarizers like BART, T5, and Pegasus exhibit self-repetition. BART is most prone, especially when fine-tuned on abstractive or formulaic data, producing unwanted artifacts in summaries.

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Machine Learning

Background:

  • Neural summarization models can generate repetitive content.
  • Understanding and mitigating self-repetition is crucial for improving summary quality.

Purpose of the Study:

  • To quantitatively and qualitatively analyze self-repetition in neural summarizer outputs.
  • To identify factors contributing to self-repetition across different architectures and datasets.

Main Methods:

  • Measured self-repetition using n-grams (length four or longer) appearing in multiple outputs.
  • Analyzed three architectures (BART, T5, Pegasus) fine-tuned on five datasets.
  • Conducted regression and qualitative analysis of generated summaries.

Main Results:

  • BART, T5, and Pegasus architectures show varying propensities for self-repetition, with BART being most susceptible.
  • Fine-tuning on abstractive or formulaic datasets correlates with increased self-repetition.
  • Qualitative analysis revealed artifacts like unrelated ads, disclaimers, and domain-specific phrases.

Conclusions:

  • Self-repetition is a significant issue in neural summarization, influenced by model architecture and training data.
  • The findings offer insights for data cleaning and developing methods to minimize repetition.
  • Further research can build upon corpus-level analysis to enhance summarizer reliability.