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Extractive single document summarization using binary differential evolution: Optimization of different sentence

Naveen Saini1, Sriparna Saha1, Dhiraj Chakraborty2

  • 1Department of Computer Science and Engineering, Indian Institute of Technology Patna, Bihar, India.

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Summary

This study introduces a novel approach to automatic text summarization using multi-objective binary differential evolution (DE). The method optimizes extractive summaries, achieving significant improvements in ROUGE scores and convergence rates on benchmark datasets.

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • The increasing volume of digital text necessitates efficient automatic text summarization systems.
  • Extractive summarization aims to select salient sentences to form a concise summary.
  • Existing methods often struggle with optimizing multiple summary quality aspects simultaneously.

Purpose of the Study:

  • To formulate extractive text summarization as a multi-objective binary optimization problem.
  • To develop and evaluate a novel optimization strategy using differential evolution (DE) and self-organizing maps (SOM).
  • To enhance summary quality by simultaneously optimizing sentence position, title similarity, length, cohesion, readability, and coverage.

Main Methods:

  • Formulated extractive text summarization as a binary optimization problem.
  • Employed multi-objective binary differential evolution (DE) with newly designed self-organizing map (SOM) based genetic operators.
  • Evaluated sentence similarity using normalized Google distance, word mover distance, and cosine similarity.
  • Utilized DUC2001, DUC2002, and CNN news datasets for performance evaluation.

Main Results:

  • Achieved superior convergence rates and ROUGE scores compared to state-of-the-art methods.
  • Demonstrated significant improvements on DUC2001 (45% ROUGE-2, 5% ROUGE-1) and DUC2002 (20% ROUGE-2, 5% ROUGE-1) datasets.
  • Confirmed the efficacy of the proposed approach on a CNN news dataset.
  • Highlighted the impact of objective functions and sentence similarity measures on summarization performance.

Conclusions:

  • The proposed multi-objective binary DE approach offers a powerful framework for extractive text summarization.
  • The integration of SOM-based genetic operators enhances optimization convergence.
  • The choice of similarity/dissimilarity measures critically influences the final summary quality.
  • This method represents a significant advancement in automatic text summarization research.