User-oriented summaries using a PSO based scoring optimization method

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Detalles Bibliográficos
Autor Principal: Villa Monte, Augusto
Otros autores o Colaboradores: Lanzarini, Laura Cristina, Bariviera, Aurelio F., Olivas Varela, José Angel
Formato: Capítulo de libro
Lengua:inglés
Temas:
resumen automático
Acceso en línea:http://dx.doi.org/10.3390/e21060617
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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 extit{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
Notas:Formato de archivo PDF. -- Este documento es producción intelectual de la Facultad de Informática - UNLP (Colección BIPA/Biblioteca)
Descripción Física:1 archivo (918,2 kB)
DOI:10.3390/e21060617

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245 1 0 |a User-oriented summaries using a PSO based scoring optimization method 
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520 |a 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 extit{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 
534 |a Entropy, 21(6), pp. 617. 
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700 1 |a Bariviera, Aurelio F.  |9 49664 
700 1 |a Olivas Varela, José Angel  |9 47334 
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