Large language models for text summarization: A comprehensive study Kumar Jeetendra1 1Assistant Professor, Atal Bihari Vajpayee University, Bilaspur, Chhattisgarh, India Online published on 30 August, 2024. Abstract In Natural Language Processing (NLP), Large Language Models (LLMs) like GPT-4 are revolutionizing text-based applications. They enable more intuitive and human-like interactions, significantly enhancing machine understanding and generation of natural language. These LLMs, powered by deep learning, have ushered in a new era of automated text summarization, which is vital for information retrieval and understanding. With the exponential growth of internet textual data, there is a growing need for efficient, accurate, and scalable text summarization methods. Large Language Models (LLMs) are ushering in a new era in Natural Language Processing (NLP), with their ability to autonomously distill key insights from vast text corpora into succinct, intelligible summaries. This in depth analysis delves into the pivotal function of LLMs in advancing the art of text summarization, making complex information readily understandable. It delves into the evolution of NLP, the development of LLMs, their architectural intricacies, and the challenges and opportunities they bring to text summarization. By the end of this exploration, readers will have a deeper understanding of these models, their capabilities, limitations, and the profound impact they can have across various applications, including news aggregation and research paper condensation. Top Keywords Large Language Model, Fine-Tuning, Transfer Learning, Text Summarization. Top |