User-Oriented Summaries Using a PSO Based Scoring Optimization Method
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 thei...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-06-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/21/6/617 |
| _version_ | 1811187259684159488 |
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| author | Augusto Villa-Monte Laura Lanzarini Aurelio F. Bariviera José A. Olivas |
| author_facet | Augusto Villa-Monte Laura Lanzarini Aurelio F. Bariviera José A. Olivas |
| author_sort | Augusto Villa-Monte |
| collection | DOAJ |
| description | 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 <i>Particle Swarm Optimization</i>. 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. |
| first_indexed | 2024-04-11T13:59:18Z |
| format | Article |
| id | doaj.art-56ac462cd4ce442b843528d00c9b4e5f |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T13:59:18Z |
| publishDate | 2019-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-56ac462cd4ce442b843528d00c9b4e5f2022-12-22T04:20:10ZengMDPI AGEntropy1099-43002019-06-0121661710.3390/e21060617e21060617User-Oriented Summaries Using a PSO Based Scoring Optimization MethodAugusto Villa-Monte0Laura Lanzarini1Aurelio F. Bariviera2José A. Olivas3Institute of Research in Computer Science LIDI (UNLP-CIC), School of Computer Science, National University of La Plata, Buenos Aires 1900, ArgentinaInstitute of Research in Computer Science LIDI (UNLP-CIC), School of Computer Science, National University of La Plata, Buenos Aires 1900, ArgentinaDepartment of Business, Universitat Rovira i Virgili, Av. Universitat 1, 43204 Reus, SpainDepartment of Information Technologies and Systems, University of Castilla-La Mancha, 13071 Ciudad Real, SpainAutomatic 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 <i>Particle Swarm Optimization</i>. 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.https://www.mdpi.com/1099-4300/21/6/617document summarizationextractive approachscoring-based representationsentence feature weightingparticle swarm optimization |
| spellingShingle | Augusto Villa-Monte Laura Lanzarini Aurelio F. Bariviera José A. Olivas User-Oriented Summaries Using a PSO Based Scoring Optimization Method Entropy document summarization extractive approach scoring-based representation sentence feature weighting particle swarm optimization |
| title | User-Oriented Summaries Using a PSO Based Scoring Optimization Method |
| title_full | User-Oriented Summaries Using a PSO Based Scoring Optimization Method |
| title_fullStr | User-Oriented Summaries Using a PSO Based Scoring Optimization Method |
| title_full_unstemmed | User-Oriented Summaries Using a PSO Based Scoring Optimization Method |
| title_short | User-Oriented Summaries Using a PSO Based Scoring Optimization Method |
| title_sort | user oriented summaries using a pso based scoring optimization method |
| topic | document summarization extractive approach scoring-based representation sentence feature weighting particle swarm optimization |
| url | https://www.mdpi.com/1099-4300/21/6/617 |
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