Text Summarization System by Deep Learning & Transfer Model

This paper evaluates recent advances in text summarization strategies, focusing at the strategies developed and deployed in a practical assignment. The project makes use of both abstractive and extractive summarization methods, employing deep gaining knowledge of models such as the Transformer architecture. This paper will discover the comparative overall performance of various models, discuss their deployment, and highlight destiny potentialities for boosting textual content summarization technology.