SAB(IO): A BIOLOGICALLY PLAUSIBLE CONNECTIONIST APPROACH TO AUTOMATIC TEXT SUMMARIZATION

dc.creatorORRU, T.
dc.creatorROSA, J. L. G.
dc.creatorANDRADE NETTO, M. L.
dc.date.accessioned2012-10-18T23:52:26Z
dc.date.accessioned2018-07-04T14:46:37Z
dc.date.available2012-10-18T23:52:26Z
dc.date.available2018-07-04T14:46:37Z
dc.date.created2012-10-18T23:52:26Z
dc.date.issued2008
dc.identifierAPPLIED ARTIFICIAL INTELLIGENCE, v.22, n.9, p.896-920, 2008
dc.identifier0883-9514
dc.identifierhttp://producao.usp.br/handle/BDPI/17486
dc.identifier10.1080/08839510802296044
dc.identifierhttp://dx.doi.org/10.1080/08839510802296044
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1614288
dc.description.abstractAn implementation of a computational tool to generate new summaries from new source texts is presented, by means of the connectionist approach (artificial neural networks). Among other contributions that this work intends to bring to natural language processing research, the use of a more biologically plausible connectionist architecture and training for automatic summarization is emphasized. The choice relies on the expectation that it may bring an increase in computational efficiency when compared to the sa-called biologically implausible algorithms.
dc.languageeng
dc.publisherTAYLOR & FRANCIS INC
dc.relationApplied Artificial Intelligence
dc.rightsCopyright TAYLOR & FRANCIS INC
dc.rightsrestrictedAccess
dc.titleSAB(IO): A BIOLOGICALLY PLAUSIBLE CONNECTIONIST APPROACH TO AUTOMATIC TEXT SUMMARIZATION
dc.typeArtículos de revistas


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