Bengali abstractive text summarization using sequence to sequence RNNs
Text summarization is one of the leading problem of natural language processing and deep learning in recent years. Text summarization contains a condensed short note on a large text document. Our purpose is to create an efficient and effective abstractive Bengali text summarizer what can generate an understandable and meaningful summary from a given Bengali text document. To do this we have collected various texts such as newspaper articles, Facebook posts etc. and to generate summary from those text we will be using our model. Our model works with bi-directional RNNs with LSTM in encoding layer and attention model at decoding layer. Our model works as sequence to sequence model to generate summary. There are some challenges we have faced while building this model such as text pre-processing, vocabulary counting, missing words counting, word embedding, unknown words find out and so on. In this model, our main goal was to make an abstractive summarizer and reduce the train loss of that. During our research experiment, we have successfully reduced the train loss to 0.008 and able to generate a fluent short summary note from a given text.
Natural Language Processing, Deep Learning, Text Pre-processing, Word-Embedding, Missing Word Counting, Vocabulary Counting, Bi-directional RNNs, Attention model, Encoding, Decoding
Md Ashraful Islam Talukder, Sheikh Abujar, Abu Kaisar Mohammad Masum, Fahad Faisal, Syed Akhter Hossain