A Bengali Text Summarization Using Encoder-Decoder Based on Social Media Dataset

Abstract

Text summarization is one of the strategies of compressing a long document to create a version of the main points of the original text. Due to the excessive amount of long posts these days, the value of summarization is born. Reading the main document and obtaining a desirable summary, time and trouble are worth it. Using machine learning and natural language processing built an automated text summarization system can solve this problem. So our proposed system will distribute an abstractive summary of a long text automatically in a period of some time. We have done the whole analysis with the Bengali text. In our designed model, we used chain to chain models of RNN with LSTM in the encrypting layer. The architecture of our model works using RNN decoder and encoder, where the encoder inputs text document and generates output as a short summary at the decoder. This system improves two things, namely summarization and establishing benchmarks performance with ignoble train loss. To train our model, we use our dataset that was created from various online media, articles, Facebook, and some people's personal posts. The challenges we face most here are Bengali text processing, limited text length, enough resources for collecting text.