This chapter explores the application of Generative AI in text summarization, a technique used to condense long pieces of text into concise, informative summaries. The primary goal is to create summaries that retain the key points of the original document while being coherent and contextually relevant. The chapter highlights two main approaches to text summarization: extractive and abstractive. Extractive summarization involves selecting key sentences directly from the source text, whereas abstractive summarization generates new sentences that convey the essential information. The chapter provides a comparative analysis of these two methods, evaluating their effectiveness in different contexts. With the advent of generative AI powered by deep learning models, such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs), the field of text summarization has seen significant advancements. These AI-driven approaches offer enhanced capabilities for producing high-quality summaries that can significantly improve information processing workflows across various industries. The chapter concludes by discussing the potential of generative AI in transforming the landscape of automated text summarization and its implications for future research and practical applications.