Revolutionizing Content Digestion: Unleashing the Power of Bidirectional and Auto-Regressive Transformers in AI-Powered Automatic Text Summarization

Abstract

Automatic text summarization has become increasingly essential in managing the overwhelming volume of textual information available across various domains. This paper explores the role of bidirectional and auto-regressive transformers, two prominent paradigms in natural language processing (NLP), in revolutionizing content digestion through AI-powered automatic text summarization. We discuss how bidirectional transformers, exemplified by models like BERT, and auto-regressive transformers, such as GPT, capture context and generate output tokens sequentially, respectively, contributing to the production of accurate and coherent summaries. By providing an overview of the challenges posed by the vast volume of textual data and the significance of automatic summarization, we delve into key advancements in NLP, emphasizing the development and applications of bidirectional and auto-regressive transformers in text summarization. Furthermore, we survey state-of-the-art models like BART and its derivatives, highlighting their convergence of bidirectional and auto-regressive techniques. Through a comprehensive analysis, we elucidate the transformative potential of bidirectional and auto-regressive transformers, offering valuable insights for researchers and practitioners in content digestion and NLP-driven knowledge extraction.