The proliferation of textual information in the digital age has made multi-document summarization (MDS) a critical tool for efficient information access. Traditional MDS approaches often struggle with input length constraints, redundancy, and coherence. In this work, we propose a novel sequential summarization paradigm: instead of generating a summary for the entire document set simultaneously, we iteratively summarize documents by integrating the current document with the previously generated summary. This progressive strategy enables incremental synthesis and alleviates token-length bottlenecks in large language models. To mitigate error accumulation and preserve factual consistency, we introduce a modular framework based on a fine-tuned pre-trained language model, augmented with lightweight auxiliary models for content selection and verification. Experiments on eight public datasets spanning news, scientific, legal, and clinical domains show up to +3 ROUGE-L and +5 BLEU over strong baselines and consistently improves BERTScore and FactCC by 1–3 points. Ablation studies validate the contribution of each module. Our findings highlight the effectiveness and scalability of iterative multi-document generation in producing coherent, concise, and factually grounded summaries.