AI-driven Text Generation for News Articles using a Deep Learning BiLSTM Model

This research study presents the development of advanced text generation using Artificial Intelligence (AI) systems. This is designed to predict, generate, and enhance the capability to produce human-like paragraphs in the domain of news articles as a paragraph for the given text. Use of a comprehensive dataset of news articles, this system implementation uses Python. Incorporation of essential libraries such as Pickle for object serialization, Numpy, and Pandas for data manipulation, and Tokenize for text preprocessing. The core of this model is built using TensorFlow-Keras, a powerful deep-learning framework that supports efficient training and deployment of neural networks. This approach ensures the handling of large datasets and facilitates the creation of an effective and scalable text generation model.