Multi-document summarization for low-resource languages faces a critical trade-off: large language models are computationally prohibitive for most institutions, while smaller models suffer from severe hallucination in abstractive generation. We address this through extractive sentence retrieval, which guarantees faithfulness while operating within constrained computational budgets. Our approach combines language-adaptive mixture-of-experts embeddings with graph neural networks that model discourse structure, addressing linguistic challenges across typologically diverse low-resource languages. With only 3.2M trainable parameters, our model requires 28 times less training time than comparable transformer-based approaches, making it practical for single-GPU environments typical in resource-limited settings. We demonstrate applicability to some main Southeast Asia (SEA) countries including Vietnam, Thailand, Laos, Indonesia, and Malaysia, representing three distinct language families: Austroasiatic, Kra-Dai, and Austronesian.