Developing a Deep Learning Model for Text Generation from a Given Set of Text

Ankita Rani

Abstract: Text generation using deep learning models has garnered significant interest in recent years. This literature review explores various approaches and techniques employed to develop deep learning models that can generate new text based on a given set of text. The review focuses on different architectures, data preprocessing techniques, training strategies, evaluation metrics, and applications of these models in the fields of natural language processing (NLP) and creative writing. Through an analysis of existing research, this review aims to provide insights into the progress made, challenges faced, and potential areas for future research in text generation using deep learning models. In this proposed model, we present a novel deep learning approach for text generation based on hierarchical Transformers. Our model aims to overcome the limitations of existing architectures and improve the generation of coherent and contextually relevant text. The proposed model leverages the power of self - attention mechanisms to effectively capture long - range dependencies in the input text and generate high - quality output. We also incorporate hierarchical structures to enable the model to understand global context while retaining fine - grained details. Through extensive experiments and evaluations, we demonstrate the effectiveness of our model in various text generation tasks.

Keywords: Text Generation, Deep Learning, LSTM, GPT