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<p>This study aims to improve extractive text summarization by dynamically optimizing the <i>min-df</i> (minimum document frequency) parameter in the GreedSum algorithm. To achieve this, three methods are proposed for dynamic tuning: a geometric approach, a percentile-based threshold, and a clustering-based adaptation strategy. These methods were applied to a large-scale dataset of 17,038 scientific articles from arXiv and PubMed. The experiments demonstrate a 2% improvement in ROUGE-1 F-measure over fixed <i>min-df</i> settings, achieving a peak ROUGE-1 F1-score of 45%. This performance surpasses several established extractive and hybrid baselines. Our contributions include a comparative evaluation of dynamic tuning strategies and the demonstration of their effectiveness for adaptive summarization. These findings confirm the potential of dynamic <i>min-df</i> optimization for generating accurate and efficient summaries, with future research focused on deep learning integration and real-time, multi-modal summarization.</p>

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Dynamic optimization of min-df in the GreedSum algorithm for enhanced extractive summarization

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月6日

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  • Shakarim Aubakirov,
  • Iskander Akhmetov,
  • Alexander Gelbukh,
  • Rustam Mussabayev

摘要

This study aims to improve extractive text summarization by dynamically optimizing the min-df (minimum document frequency) parameter in the GreedSum algorithm. To achieve this, three methods are proposed for dynamic tuning: a geometric approach, a percentile-based threshold, and a clustering-based adaptation strategy. These methods were applied to a large-scale dataset of 17,038 scientific articles from arXiv and PubMed. The experiments demonstrate a 2% improvement in ROUGE-1 F-measure over fixed min-df settings, achieving a peak ROUGE-1 F1-score of 45%. This performance surpasses several established extractive and hybrid baselines. Our contributions include a comparative evaluation of dynamic tuning strategies and the demonstration of their effectiveness for adaptive summarization. These findings confirm the potential of dynamic min-df optimization for generating accurate and efficient summaries, with future research focused on deep learning integration and real-time, multi-modal summarization.