Text generation is the process of automatically producing coherent and meaningful text, which can be in the form of sentences, paragraphs or even entire documents. It involves various techniques, which can be found under the field such as Natural Language Processing (NLP) and deep learning algorithms, to analyze input data and generate human-like text. The goal is to create text that is not only grammatically correct but also contextually appropriate and engaging for the intended audience. In advance we want to focus on text summarization because for generating text includes correct formation of sentence and reduce the user difficulty. Here we use deep learning techniques like Recurrent neural network (RNN), Generative pre-trained transformer (GPT), Bi-directional encoder representations from transformers (BERT). Text summarization models often face challenges such as lack of precision, vocabulary limitations, incorrect sentences, and false information.