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Text Analysis for Bengali Text Summarization Using Deep Learning

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dc.contributor.author Munzir, Abdullah Al
dc.contributor.author Rahman, MD. Lutfor
dc.date.accessioned 2019-09-22T04:48:26Z
dc.date.available 2019-09-22T04:48:26Z
dc.date.issued 2019-05-03
dc.identifier.uri http://hdl.handle.net/123456789/3434
dc.description.abstract Text summarization is an approach by which the size of one or more document is shorten and the shorten passage presents the core information of the document. In this modern era of information technology, we are over flooded with online data which raised the necessity of summary of the original text. Many methods have already implemented for English text and the effort for Bengali text are gaining alongside. In this paper we propose an extractive text summarization technique based on a deep learning model of Recurrent Neural Network (RNN). Our method is to classify the sentences as significant or not for the summary. We have used Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU) for the backpropagation method. Between them we found Long Short-Term Memory (LSTM) more promising and we achieved average F1 scores- 0.63, 0.59, 0.56 for Rouge-1, Rouge-2 and Rouge-3 in some respects. en_US
dc.language.iso en_US en_US
dc.publisher Daffodil International University en_US
dc.relation.ispartofseries ;P13304
dc.subject Computer Science en_US
dc.subject Deep Neural Network en_US
dc.subject Sequence Classification en_US
dc.subject Bengali Text en_US
dc.title Text Analysis for Bengali Text Summarization Using Deep Learning en_US
dc.type Other en_US

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