Mostrar el registro sencillo del ítem

dc.contributor.authorMuthu, Bala Anand
dc.contributor.authorCb, Sivaparthipan
dc.contributor.authorKumar, Priyan Malarvizhi
dc.contributor.authorKadry, Seifedine
dc.contributor.authorHsu, Ching-Hsien
dc.contributor.authorSanjuán Martínez, Óscar
dc.contributor.authorGonzález-Crespo, Rubén
dc.date2021
dc.date.accessioned2022-04-25T12:20:45Z
dc.date.available2022-04-25T12:20:45Z
dc.identifier.issn2375-4699
dc.identifier.urihttps://reunir.unir.net/handle/123456789/12924
dc.description.abstractThere is an exponential growth of text data over the internet, and it is expected to gain significant growth and attention in the coming years. Extracting meaningful insights from text data is crucially important as it offers value-added solutions to business organizations and end-users. Automatic text summarization (ATS) automates text summarization by reducing the initial size of the text without the loss of key information elements. In this article, we propose a novel text summarization algorithm for documents using Deep Learning Modifier Neural Network (DLMNN) classifier. It generates an informative summary of the documents based on the entropy values. The proposed DLMNN framework comprises six phases. In the initial phase, the input document is pre-processed. Subsequently, the features are extracted using pre-processed data. Next, the most appropriate features are selected using the improved fruit fly optimization algorithm (IFFOA). The entropy value for every chosen feature is computed. These values are then classified into two classes, (a) highest entropy values and (b) lowest entropy values. Finally, the class that holds the highest entropy values is chosen, representing the informative sentences that form the last summary. The results observed from the experiment indicate that the DLMNN classifier gives 81.56, 91.21, and 83.53 of sensitivity, accuracy, specificity, precision, and f-measure. Whereas the existing schemes such as ANN relatively provide lesser value in contrast to DLMNN.es_ES
dc.language.isoenges_ES
dc.publisherAssociation for Computing Machineryes_ES
dc.relation.ispartofseries;vol. 20, nº 3
dc.relation.urihttps://dl.acm.org/doi/10.1145/3392048es_ES
dc.rightsrestrictedAccesses_ES
dc.subjectautomatic text summarization (ATS)es_ES
dc.subjectdeep learning modified neural network (DLMNN)es_ES
dc.subjectextractive summarizationes_ES
dc.subjectimproved fruit fly optimization algorithm (IFFOA)es_ES
dc.subjectkrill kerd optimization algorithm (KHOA)es_ES
dc.subjectsingle document summarizationes_ES
dc.subjectScopuses_ES
dc.subjectJCRes_ES
dc.titleA Framework for Extractive Text Summarization Based on Deep Learning Modified Neural Network Classifieres_ES
dc.typearticlees_ES
reunir.tag~ARIes_ES
dc.identifier.doihttps://doi.org/10.1145/3392048


Ficheros en el ítem

FicherosTamañoFormatoVer

No hay ficheros asociados a este ítem.

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem