The evolution of Question Answering Systems: A comprehensive survey of trends and challenges - Dataset

Dataset of "The evolution of Question Answering Systems: A comprehensive survey of trends and challenges".

This survey provides a comprehensive review of the evolution of question answering (QA) systems by synthesizing insights from 500 papers published between 2020 and 2025. Spurred by advancements in deep neural networks and transformer-based models, QA research has experienced a transformative leap in natural language understanding (NLU). We employ a narrative review methodology that merges systematic data collection and preprocessing with a two-tiered classification pipeline, integrating rule-based heuristics and large language model–driven analysis, to categorize the literature into key dimensions such as open-domain, conversational, domain-specific, knowledge-base, multi-modal, and evaluation-focused approaches.

Our findings reveal an early surge in QA publications, propelled by pioneering neural architectures, followed by a period of consolidation characterized by incremental innovations and hybrid methods. Despite these gains, significant challenges remain, including data dependence, semantic ambiguity, multi-turn context retention, and model interpretability. Analyzing publication and citation trends across QA subfields underscores the field's progression from initial breakthroughs to the systematic refinement of established techniques.

Finally, the survey examines the implications of these trends for future research, advocating for standardized evaluation metrics, adaptive learning strategies, and ethically informed design practices. By connecting historical perspectives with contemporary advances, this work offers a robust roadmap for next-generation QA systems, poised for impactful applications in education, adaptive learning, information retrieval, and AI-driven personalization.