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.

In this paper, a deep multi-kernel clustering network, named DMKCN, is proposed to learn a high-quality and structurally separable kernel representation for the clustering task. Specifically, a multi-kernel learner is proposed to choose a suitable kernel function by learning a suitable combination of kernel functions automatically. A kernel-aid encoder module, consisting of a series of multi-kernel learners, is proposed to learn the structurally separable kernel representation. Besides, a dual self-supervised mechanism, consisting of a kernel self-supervised strategy and a representation self-supervised strategy, is designed to uniformly optimize the kernel representation learning and structural partition. The kernel self-supervised strategy is developed to supervise the multi-kernel learners with the consideration of an objective of clustering task, the representation self-supervised strategy is developed to guide the optimization of kernel representation learning by reconstructing the raw data. Extensive experiments on six real-world datasets demonstrate the outstanding performance of our proposed DMKCN.