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DiCo-EXT: Diversity and Consistency-Guided Framework for Extractive Summarization.

Yiming Wang1, Jindong Zhang2

  • 1College of Computer Science and Technology, Jilin University, Changchun 130012, China.

Entropy (Basel, Switzerland)
|January 28, 2026
PubMed
Summary

This study introduces DiCo-EXT, a new framework for extractive summarization that reduces redundant information. It achieves better summary diversity and semantic consistency without sacrificing ROUGE scores.

Keywords:
ROUGE evaluation limitationsdiversity penalty lossextractive summarizationredundancy reductionsemantic similarity consistency (SSC)

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Machine Learning

Background:

  • Extractive summarization commonly uses ROUGE scores, which can lead to redundant sentences.
  • Existing models often prioritize n-gram overlap, neglecting semantic diversity and coherence.

Purpose of the Study:

  • To propose DiCo-EXT, a novel training framework for extractive summarization.
  • To address redundancy and improve semantic diversity in generated summaries.

Main Methods:

  • Integrated two differentiable loss terms: semantic consistency and diversity penalty.
  • Optimized these terms with the base loss in a standard extractive model.
  • Avoided heuristics and multi-stage post-processing.

Main Results:

  • Achieved lower redundancy and higher lexical diversity in summaries.
  • Maintained comparable ROUGE scores against a strong baseline.
  • Demonstrated effectiveness on CNN/DailyMail, XSum, and WikiHow datasets.

Conclusions:

  • Simple training objectives can effectively balance coverage and redundancy.
  • DiCo-EXT enhances summary quality without increasing model size or complexity.
  • The framework offers a practical approach to improving extractive summarization.