Scopus Indexed Paper

Paper Details


Title
Text analysis for Bengali Text Summarization using Deep Learning
Abstract
Text 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.
Keywords
LSTM, GRU-RNN, Extractive summary, Bengali text, Sequence classification
Authors
Abdullah Al Munzir, Md. Lutfor Rahman, Sheikh Abujar, Ohidujjaman, Syed Akhter Hossain
Phone
Journal or Conference Name
https://ieeexplore.ieee.org/document/8944562
Publish Year
2019
Indexing
Scopus