Text Analysis for Bengali Text Summarization Using Deep Learning

dc.contributor.authorMunzir, Abdullah Al
dc.contributor.authorRahman, Md. Lutfor
dc.contributor.authorAbujar, Sheikh
dc.contributor.authorOhidujjaman
dc.contributor.authorHossain, Syed Akhter
dc.date.accessioned2021-12-30T04:01:45Z
dc.date.available2021-12-30T04:01:45Z
dc.date.issued2019-12-30
dc.description.abstractText summarization is an approach by which the size of one or more document is shortened 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) for single document summary. 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) based RNN. Between them, we found 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.
dc.identifier.otherhttp://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/6599
dc.identifier.urihttp://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/6599
dc.language.isoen_US
dc.publisher10th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2019, IEEE
dc.sourceDIU Institutional Repository
dc.subjectText data mining
dc.subjectText analysis
dc.subjectMachine learning
dc.subjectDeep learning
dc.titleText Analysis for Bengali Text Summarization Using Deep Learning
dc.typeArticle

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