Information compression and semantic coherence in long texts are persistent challenges in summary generation models. To address this issue, this study proposes a summary generation model integrating content-guided and multi-scale attention. The model adopts a dual-branch architecture to jointly model multi-granularity semantics and utilizes a content-guided mechanism to focus on key information relevant to the summary. Based on the conventional BERT-Transformer framework, a dual-branch structure is introduced to enhance semantic representation, and a cross-branch fusion mechanism (MSAA-SAM) is designed to achieve semantic alignment and unified representation. In addition, the pointer-generator network is improved by incorporating a global sentence vector guidance mechanism to enhance generation control, thereby improving key information extraction and reducing redundancy in long-text summarization. Experimental results on the NLPCC 2017 and LCSTS datasets demonstrate that the proposed model outperforms mainstream baseline models in generative summarization tasks, verifying its comprehensive advantages in semantic modeling, generation quality, and control capability.