Most existing text summarization methods are designed for short texts and fail to perform well on long documents, especially in low-resource languages such as Tibetan. To address this challenge, we propose DiSG, a Discourse-aware, Stage-based, and Generative summarization framework built on pre-trained language models. DiSG leverages discourse structure to guide a multi-stage process, enabling effective extraction and generation of summaries from long text. Experiments on the TiLTS dataset show that DiSG achieves a 2.73-point improvement in ROUGE-L over the best baseline, demonstrating its superiority in producing coherent and complete summaries.