Autor según el artículo: Villa-Monte A; Lanzarini L; Corvi J; Bariviera AF
Departamento: Gestió d'Empreses
Autor/es de la URV: Fernández Bariviera, Aurelio
Palabras clave: Text summarization Sentence scoring Neural networks Feature selection Extractive summaries
Resumen: © 2020 - IOS Press and the authors. All rights reserved. Currently, each person produces 1.7MB of information every second in different formats. However, the vast majority of information is text. This has increased the interest to study techniques to automate the identification of the relevant portions of text documents in order to offer as a result an automatic summary. This article presents a technique to extract the most representative sentences of a document taking into account by the user's criteria. These criteria are learned using a neural network, from a minimum set of documents whose sentences have been rated by the user in terms of importance. To verify the performance of the proposed methodology, we used 220 scientific articles from the PLOS Medicine journal published between 2004 and 2016. The results obtained have been very satisfactory.
Áreas temáticas: Statistics and probability Interdisciplinar General engineering Ensino Engineering (miscellaneous) Engineering (all) Engenharias iv Engenharias iii Economia Computer science, artificial intelligence Ciências ambientais Ciência da computação Biotecnología Artificial intelligence Administração pública e de empresas, ciências contábeis e turismo
Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
ISSN: 10641246
Direcció de correo del autor: aurelio.fernandez@urv.cat
Identificador del autor: 0000-0003-1014-1010
Fecha de alta del registro: 2024-04-27
Versión del articulo depositado: info:eu-repo/semantics/acceptedVersion
Enlace a la fuente original: https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs179648
Referencia al articulo segun fuente origial: Journal Of Intelligent & Fuzzy Systems. 38 (5): 5579-5588
Referencia de l'ítem segons les normes APA: Villa-Monte A; Lanzarini L; Corvi J; Bariviera AF (2020). Document summarization using a structural metrics based representation. Journal Of Intelligent & Fuzzy Systems, 38(5), 5579-5588. DOI: 10.3233/JIFS-179648
URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
DOI del artículo: 10.3233/JIFS-179648
Entidad: Universitat Rovira i Virgili
Año de publicación de la revista: 2020
Tipo de publicación: Journal Publications
Repositori URV