Text summarization is a pivotal application of NLP that condenses lengthy documents or articles into shorter, coherent representations while retaining the essential information. Through various algorithms and techniques, NLP models identify significant sentences, key phrases, or essential concepts within the text to generate concise summaries. Extractive summarization involves selecting and stitching together important segments directly from the original text, often based on relevance, importance, or frequency of occurrence. On the other hand, abstractive summarization goes beyond extraction, generating novel sentences that convey the core meaning while potentially rephrasing and restructuring the content. NLP-powered summarization systems play a crucial role in information retrieval, aiding in quick comprehension and accessibility of vast amounts of text across diverse domains such as news articles, research papers, and legal documents. ChatGPT boasts impressive text summarization capabilities, harnessing its advanced Natural Language Processing (NLP) architecture to distill lengthy conversations, articles, or documents into concise, coherent summaries. Leveraging its vast understanding of language semantics, context, and syntax, ChatGPT effectively identifies key points, essential information, and significant passages within the text. Its summarization prowess encompasses extractive and abstractive techniques, allowing it to select important segments directly from the input while generating novel, coherent sentences that capture the essence of the content.