Multi-turn dialogue summarization aims to efficiently extract core information from vast amounts of conversational data. However, this task often causes “structure-saliency conflict” when balancing the structural perception and saliency focus of the summary, resulting in chaotic summary logic or vacuous content. This paper proposes a guided attention mechanism based on the synergy of structure and saliency. First, the macro-topic flow of the dialogue is predicted, and a structural attention mask is constructed to impose hard constraints on the summary range, ensuring the overall logical coherence of the summary to alleviate the problem of logical confusion; secondly, within the constraint range, the saliency of each discourse is scored by fusing multi-dimensional features, and the attention weight is dynamically guided softly to ensure the focus of key content thereby mitigating the issue of vacuous or generic content. To verify the mechanism, the STGSum summary model is constructed. Experiments on two public datasets, CSDS and DialogSum, show that the STGSum model performs significantly better than mainstream baseline models such as TGDS and TODS in key indicators such as ROUGE, especially on complex, structured dialogues, demonstrating excellent robustness. This study provides an effective solution for generating high-quality dialogue summaries with clear logic and prominent focus.