A comprehensive survey on answer generation methods using NLP

Recent advancements in question-answering systems have significantly enhanced the capability of computers to understand and respond to queries in natural language. This paper presents a comprehensive review of the evolution of question answering systems, with a focus on the developments over the last few years. We examine the foundational aspects of a question answering framework, including question analysis, answer extraction, and passage retrieval. Additionally, we delve into the challenges that question answering systems encounter, such as the intricacies of question processing, the necessity of contextual data sources, and the complexities involved in real-time question answering. Our study categorizes existing question answering systems based on the types of questions they address, the nature of the answers they produce, and the various approaches employed to generate these answers. We also explore the distinctions between opinion-based, extraction-based, retrieval-based, and generative answer generation. The classification provides insight into the strengths and limitations of each method, paving the way for future innovations in the field. This review aims to offer a clear understanding of the current state of question answering systems and to identify the scaling needed to meet the rising expectations and demands of users for coherent and accurate automated responses in natural language.