Affiliations: [a] Instituto de Investigación en InformáticaLIDI (Centro CICPBA), Facultad de Informática, Universidad Nacional de La Plata, 50 y 120 S/N La Plata, Buenos Aires, Argentina
| [b] Universitat Rovira i Virgili, Department of Business, Av. Universitat 1 Reus, Spain
Correspondence:
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Corresponding author. Aurelio F. Bariviera, Universitat Rovira i Virgili, Department of Business, Av. Universitat 1 43204 Reus, Spain. E-mail: aurelio.fernandez@urv.cat.
Note: [1] Post-Doctoral Fellow at National University of La Plata.
Abstract: 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.
