A Review of Multi-Label and Multi-Document Research in Community Question Answering

Community Question Answering (CQA) platforms function as continuously evolving repositories of user-generated knowledge, where questions are often informal, intents may be multiple, evidence is distributed across sources, and knowledge changes over time. These characteristics limit the applicability of conventional single-label and single-document approaches to complex information needs. This review traces the evolution of CQA from retrieval and ranking through neural semantic matching and pretrained Transformer models to retrieval-augmented generation and large language models, with particular emphasis on multi-label question understanding and multi-document evidence integration. Existing studies are compared from the perspectives of semantic representation, intent modeling, evidence retrieval and fusion, knowledge grounding, and answer reliability. The literature shows that substantial progress has been made in individual capabilities, but a unified mechanism that coordinates multi-label intent interpretation, dynamic retrieval, evidence verification, and grounded generation remains insufficiently developed. Future research should therefore move toward closed-loop architectures and evaluation frameworks that jointly measure label identification, evidence coverage, source attribution, and final answer quality in realistic community settings.