In order to overcome challenges of traditional deliberation approaches that often silo information exchange between synchronous and asynchronous modes therefore hindering effective deliberation, we present a hybrid framework combining Large Language Models (LLMs) and human-in-the-loop curation to generate argument maps from deliberation transcripts. This approach aims to enhance the efficiency and quality of the generated argument maps, promote transparency, and connect the asynchronous and synchronous deliberation modes. Finally, we outline a realistic deliberation scenario where this process can be successfully integrated.