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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Opinion texts summarization based on texts concepts with multi-objective pruning approach.

Sajjad Jahanbakhsh Gudakahriz1, Amir Masoud Eftekhari Moghadam1, Fariborz Mahmoudi2

  • 1Department of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.

The Journal of Supercomputing
|October 17, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an unsupervised extractive summarization method for opinion texts, improving text analysis on social media. The approach enhances summarization performance using manifold learning and concept-based pruning.

Keywords:
Concepts of textsCoverageMulti-objective pruningOpinion texts summarizationRedundancyRelevancy

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Data Mining

Background:

  • Social networks generate vast amounts of opinion text, making manual analysis challenging.
  • Automatic summarization aids users in efficiently processing large volumes of text data.

Purpose of the Study:

  • To develop an unsupervised extractive summarization method for opinion texts.
  • To address the complexity and high dimensionality of social media opinion data.
  • To improve summarization performance by focusing on text concepts.

Main Methods:

  • Employed manifold learning to handle high-dimensional opinion text data.
  • Utilized K-Means algorithm for text clustering.
  • Implemented a concept-based summarization approach with multi-objective pruning (relevancy, redundancy, coverage).

Main Results:

  • The proposed method demonstrated superior performance compared to the MOOTweetSumm approach.
  • Achieved an 11% improvement in ROGUE-1 scores.
  • Achieved a 9% improvement in ROGUE-L scores.

Conclusions:

  • The unsupervised extractive summarization method effectively handles complex, high-dimensional opinion texts.
  • Concept-based summarization with multi-objective pruning enhances text summarization quality.
  • The method offers a significant improvement for analyzing social media opinion data.