Understanding question-answering systems: Evolution, applications, trends, and challenges

Abstract

Question answering (QA) systems have garnered significant attention in recent years due to their potential to bridge the gap between human language understanding and machine intelligence. Consequently, a wide variety of approaches have been developed, each tailored to specific tasks. In this survey paper, we provide a comprehensive overview of three prominent QA paradigms: Extractive, generative, and Visual QA. We discuss the underlying principles, methodologies, applications, challenges, and recent trends in each of these areas. By synthesizing insights from the existing literature and research findings, we aim to provide a holistic understanding of extractive, generative, and Visual QA systems and offer insights into their strengths, limitations, and future directions.