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    A Framework for Extractive Text Summarization Based on Deep Learning Modified Neural Network Classifier

    Autor: 
    Muthu, Bala Anand
    ;
    Cb, Sivaparthipan
    ;
    Kumar, Priyan Malarvizhi
    ;
    Kadry, Seifedine
    ;
    Hsu, Ching-Hsien
    ;
    Sanjuán Martínez, Óscar
    ;
    González-Crespo, Rubén
    Fecha: 
    2021
    Palabra clave: 
    automatic text summarization (ATS); deep learning modified neural network (DLMNN); extractive summarization; improved fruit fly optimization algorithm (IFFOA); krill kerd optimization algorithm (KHOA); single document summarization; Scopus; JCR
    Revista / editorial: 
    Association for Computing Machinery
    Tipo de Ítem: 
    article
    URI: 
    https://reunir.unir.net/handle/123456789/12924
    DOI: 
    https://doi.org/10.1145/3392048
    Dirección web: 
    https://dl.acm.org/doi/10.1145/3392048
    Resumen:
    There 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.
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