Extracting an Optimal Set of Linguistic Summaries using Genetic Algorithm Combined with Greedy Strategy

Phạm Thị Lan, Nguyễn Cát Hồ, Phạm Đình Phong

  • Phạm Thị Lan Department of Information Technology, Hanoi University of Education
Keywords: Linguistic data summary, hedge algebras, linguistic frame of cognition, genetic algorithm, greedy strategy

Abstract

The goal of extracting linguistic data summaries is to produce summary sentences expressed in natural language which represent knowledge hidden in numerical dataset. At the most general level, human users can get a very large number of linguistic summaries. In this paper, we propose a model of genetic algorithm combined with greedy strategy to extract an optimal set of linguistic summaries based on the evaluation measures of goodness and diversity of the set of linguistic summaries. The experimental results on creep dataset have demonstrated the outperformance of the proposed model of genetic algorithm combined with greedy strategy in comparison with the existing genetic algorithm models in extracting linguistic summaries from data.

Author Biography

Phạm Thị Lan, Department of Information Technology, Hanoi University of Education

Phạm Thị Lan1, Nguyễn Cát Hồ2, 3, Phạm Đình Phong4

  1. Department of Information Technology, Hanoi University of Education
  2. Theoretical and Applied Research Institute, Duy Tan University
  3. Department of Information Technology, Duy Tan University
  4. Department of Information Technology, University of Transport and Communications

Contact: Pham Thi Lan, ptlan@hnue.edu.vn

Published
2021-04-27