Next Article in Journal
Signatures of Quantum Mechanics in Chaotic Systems
Previous Article in Journal
Endemics and Cosmopolitans: Application of Statistical Mechanics to the Dry Forests of Mexico
Previous Article in Special Issue
Competitive Particle Swarm Optimization for Multi-Category Text Feature Selection
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessArticle

User-Oriented Summaries Using a PSO Based Scoring Optimization Method

1
Institute of Research in Computer Science LIDI (UNLP-CIC), School of Computer Science, National University of La Plata, Buenos Aires 1900, Argentina
2
Department of Business, Universitat Rovira i Virgili, Av. Universitat 1, 43204 Reus, Spain
3
Department of Information Technologies and Systems, University of Castilla-La Mancha, 13071 Ciudad Real, Spain
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(6), 617; https://doi.org/10.3390/e21060617
Received: 2 April 2019 / Revised: 3 June 2019 / Accepted: 18 June 2019 / Published: 22 June 2019
(This article belongs to the Special Issue Unconventional Methods for Particle Swarm Optimization)
  |  
PDF [897 KB, uploaded 22 June 2019]
  |  

Abstract

Automatic text summarization tools have a great impact on many fields, such as medicine, law, and scientific research in general. As information overload increases, automatic summaries allow handling the growing volume of documents, usually by assigning weights to the extracted phrases based on their significance in the expected summary. Obtaining the main contents of any given document in less time than it would take to do that manually is still an issue of interest. In this article, a new method is presented that allows automatically generating extractive summaries from documents by adequately weighting sentence scoring features using Particle Swarm Optimization. The key feature of the proposed method is the identification of those features that are closest to the criterion used by the individual when summarizing. The proposed method combines a binary representation and a continuous one, using an original variation of the technique developed by the authors of this paper. Our paper shows that using user labeled information in the training set helps to find better metrics and weights. The empirical results yield an improved accuracy compared to previous methods used in this field. View Full-Text
Keywords: document summarization; extractive approach; scoring-based representation; sentence feature weighting; particle swarm optimization document summarization; extractive approach; scoring-based representation; sentence feature weighting; particle swarm optimization
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Villa-Monte, A.; Lanzarini, L.; Bariviera, A.F.; Olivas, J.A. User-Oriented Summaries Using a PSO Based Scoring Optimization Method. Entropy 2019, 21, 617.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top