DualView is a new adaptive local-global fusion framework for multi-hop document reranking in question answering systems. It consists of two components: a Local Scorer that uses cross-attention to assess query-document relevance and a Global Scorer that models inter-document dependencies using Transformer-based context aggregation. The two scorers are dynamically combined through an adaptive gate conditioned on query semantics. The framework is designed to operate as a lightweight post-retrieval stage and achieves competitive results in multi-hop question answering tasks, particularly on the MuSiQue dataset, with a 4.0 ms latency and 99.4% Top-4 Recall. It outperforms larger cross-encoders with 5-6 times lower latency, making it a promising solution for knowledge-intensive applications.