The exponential growth of academic publications has significantly increased the complexity of synthesizing knowledge across various disciplines. Researchers often struggle to manually analyze vast volumes of literature, a process that is both time-consuming and prone to biases. These challenges highlight the urgent need for innovative solutions that can streamline the literature review process and improve the quality of knowledge synthesis.
This paper proposes a theoretical framework based on Retrieval-Augmented Generation (RAG) to automate the aggregation and summarization of academic literature. By integrating semantic search, generative AI, and knowledge graph technology, the framework offers a comprehensive solution to efficiently retrieve, synthesize, and contextualize key findings from relevant academic works. The use of knowledge graphs enhances the identification of research trends and gaps, offering researchers a deeper understanding of interconnected topics and areas requiring further exploration.
Key contributions of this work include the conceptualization of the RAG-based framework and the introduction of a theoretical evaluation methodology. The evaluation metrics focus on semantic relevance, contextual coherence, source diversity, and usability, providing a robust foundation for assessing the framework’s potential. By reducing the time and effort required for literature reviews, this framework aims to accelerate innovation, facilitate interdisciplinary collaboration, and transform traditional research workflows.