Author, as appears in the article.: Villa-Monte A; Lanzarini L; Corvi J; Bariviera AF
Department: Gestió d'Empreses
URV's Author/s: Fernández Bariviera, Aurelio
Keywords: Text summarization Sentence scoring Neural networks Feature selection Extractive summaries
Abstract: © 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.
Thematic Areas: 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
licence for use: https://creativecommons.org/licenses/by/3.0/es/
ISSN: 10641246
Author's mail: aurelio.fernandez@urv.cat
Author identifier: 0000-0003-1014-1010
Record's date: 2024-04-27
Papper version: info:eu-repo/semantics/acceptedVersion
Link to the original source: https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs179648
Papper original source: Journal Of Intelligent & Fuzzy Systems. 38 (5): 5579-5588
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
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Article's DOI: 10.3233/JIFS-179648
Entity: Universitat Rovira i Virgili
Journal publication year: 2020
Publication Type: Journal Publications