"In recent years, subject generation has emerged as

one of the major challenges for deep learning and natural

language processing (NLP). A brief comment on a lengthy email

body is condensed in the subject generation. Our goal is to

develop a Bengali subject generator that is effective and efficient

and can produce a clear and insightful subject from a given

Bengali email body. To do this, we have gathered a variety of

emails body, including educational, commercial, etc. And will

use our model to generate subjects from those texts. Our model

uses bi-directional RNNs in the encoding layer along with

LSTMs and the decoding layer we used an attention model. Our

model generates subjects using a sequence-to-sequence model.

While developing this model, we encountered difficulties with

text pre-processing, missing word counting, vocabulary

counting, identifying unfamiliar words, word embedding, and

other tasks. Our primary objectives in this model were to

generate a subject and lessen its training loss of it. By crafting a

succinct, fluent topic from an email body, we effectively

decreased the training loss in our study trial to 0.001"