Abstract:
Text summarization defines artifices of reducing a long document to create a tale of the main aims of
the original text. Due to the huge number of long posts nowadays, the value of summarization is
produced. Reading the main document and getting a desirable summary, time and stress are worth it.
Using Machine learning & 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 full analysis with the Bengali text. In our
planned model we used a chain-to-chain models of RNN with LSTM in the encrypting layer. The
structure of our model works applying an RNN decoder and encoder where the encoder inputs text
documents and creative output as a short summary at the decoder. This system improves two things
namely, summarization & establishing great 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 difficulties we face most here are Bengali text processing, limited text length,
enough resources for collecting text.
