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User-Oriented Summaries Using a PSO Based Scoring Optimization Method.

Augusto Villa-Monte1, Laura Lanzarini1, Aurelio F Bariviera2

  • 1Institute of Research in Computer Science LIDI (UNLP-CIC), School of Computer Science, National University of La Plata, Buenos Aires 1900, Argentina.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary

This study introduces a new method for automatic text summarization using Particle Swarm Optimization (PSO). The approach improves summary accuracy by weighting sentence features based on user-labeled data.

Keywords:
document summarizationextractive approachparticle swarm optimizationscoring-based representationsentence feature weighting

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • The increasing volume of digital documents necessitates efficient methods for information extraction.
  • Automatic text summarization aids in managing information overload across various domains like medicine and law.
  • Current summarization techniques often rely on assigning significance weights to extracted phrases.

Purpose of the Study:

  • To present a novel method for generating extractive text summaries.
  • To enhance the accuracy of automatic summarization through optimized feature weighting.
  • To identify sentence features that align with human summarization criteria.

Main Methods:

  • Utilizing Particle Swarm Optimization (PSO) for sentence scoring feature weighting.
  • Combining binary and continuous representations within the PSO algorithm.
  • Incorporating user-labeled data into the training set to refine metrics and weights.

Main Results:

  • The proposed method demonstrates improved accuracy in generating extractive summaries.
  • Empirical results show superior performance compared to existing summarization techniques.
  • Effective identification of key features relevant to human summarization criteria.

Conclusions:

  • The novel PSO-based method offers a significant advancement in automatic text summarization.
  • User-labeled data is crucial for optimizing summarization metrics and achieving higher accuracy.
  • This approach provides a more efficient way to obtain the main contents of documents.