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User-Oriented Summaries Using a PSO Based Scoring Optimization Method

  • Datos identificativos

    Identificador: imarina:5639536
    Autores:
    Villa-Monte A, Lanzarini L, Bariviera A, Olivas J
    Resumen:
    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.
  • Otros:

    Autor según el artículo: Villa-Monte A, Lanzarini L, Bariviera A, Olivas J
    Departamento: Gestió d'Empreses
    Autor/es de la URV: Fernández Bariviera, Aurelio
    Palabras clave: Text mining Sentence feature weighting Scoring-based representation Particle swarm optimization Extractive approach Document summarization Automatic summarization
    Resumen: 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.
    Áreas temáticas: Saúde coletiva Physics, multidisciplinary Physics and astronomy (miscellaneous) Physics and astronomy (all) Medicina ii Medicina i Mathematical physics Matemática / probabilidade e estatística Interdisciplinar Information systems Geociências General physics and astronomy Filosofía Engenharias iv Engenharias iii Electrical and electronic engineering Educação física Ciências biológicas i Ciência da computação Astronomia / física
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 10994300
    Direcció de correo del autor: aurelio.fernandez@urv.cat
    Identificador del autor: 0000-0003-1014-1010
    Fecha de alta del registro: 2023-07-31
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://www.mdpi.com/1099-4300/21/6/617
    Referencia al articulo segun fuente origial: Entropy. 21 (6):
    Referencia de l'ítem segons les normes APA: Villa-Monte A, Lanzarini L, Bariviera A, Olivas J (2019). User-Oriented Summaries Using a PSO Based Scoring Optimization Method . Entropy, 21(6), -. DOI: 10.3390/e21060617
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    DOI del artículo: 10.3390/e21060617
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2019
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Electrical and Electronic Engineering,Information Systems,Mathematical Physics,Physics and Astronomy (Miscellaneous),Physics, Multidisciplinary
    Text mining
    Sentence feature weighting
    Scoring-based representation
    Particle swarm optimization
    Extractive approach
    Document summarization
    Automatic summarization
    Saúde coletiva
    Physics, multidisciplinary
    Physics and astronomy (miscellaneous)
    Physics and astronomy (all)
    Medicina ii
    Medicina i
    Mathematical physics
    Matemática / probabilidade e estatística
    Interdisciplinar
    Information systems
    Geociências
    General physics and astronomy
    Filosofía
    Engenharias iv
    Engenharias iii
    Electrical and electronic engineering
    Educação física
    Ciências biológicas i
    Ciência da computação
    Astronomia / física
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