Understanding question-answering systems: Evolution, applications, trends, and challenges
Amer Farea, Frank Emmert‐Streib · Engineering Applications of Artificial Intelligence · 2025
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.