Automatically generating concise, informative comments for source code can lighten documentation effort and accelerate program comprehension. Retrieval-augmented approaches first fetch code snippets with existing comments and then synthesize a new comment, yet retrieval and generation are typically optimized in isolation, allowing irrelevant neighbors to propagate noise downstream. To tackle the issue, we propose a novel approach named RAGSum with the aim of both effectiveness and efficiency in recommendations. RAGSum is built on top of fuse retrieval and generation using a single CodeT5 backbone. We report preliminary results on a unified retrieval-generation framework built on CodeT5. A contrastive pre-training phase shapes code embeddings for nearest-neighbor search; these weights then seed end-to-end training with a composite loss that (i) rewards accurate top-k retrieval; and (ii) minimizes comment generation error. More importantly, a lightweight self-refinement loop is deployed to polish the final output. We evaluated the framework on three cross-language benchmarks (Java, Python, C) and compared it with three well-established baselines. The results show that our approach substantially outperforms the baselines with respect to the BLEU, METEOR, and ROUTE-L scores. These early findings indicate that tightly coupling retrieval and generation can raise the ceiling for comment automation and motivate forthcoming industrial replications and qualitative developer studies.