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: 2023-02-19

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

URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/

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.

DOI del artículo: 10.3233/JIFS-179648

Entidad: Universitat Rovira i Virgili

Año de publicación de la revista: 2020

Tipo de publicación: Proceedings Paper