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.

In many practical tasks, there are usually two kinds of common information: cheap unlabeled data and domain knowledge in the form of symbols. There are some attempts using one single information source, such as semi-supervised learning and abductive learning. However, there is little work to use these two kinds of information sources at the same time, because it is very difficult to combine symbolic logical representation and numerical model optimization effectively. The learning becomes even more challenging when the domain knowledge is insufficient. In this paper, we present an attempt-Semi-Supervised ABductive Learning (SS-ABL) framework. In this framework, semi-supervised learning is trained via pseudo labels of unlabeled data generated by abductive learning, and the background knowledge is refined via the label distribution predicted by semi-supervised learning. The above framework can be optimized iteratively and can be naturally interpretable. The effectiveness of our framework has been fully verified in the theft judicial sentencing of real legal documents. In the case of missing sentencing elements and mixed legal rules, our framework is apparently superior to many existing baseline practices, and provides explanatory assistance to judicial sentencing.

This paper investigates a framework that discovers pair-wise constraints for semi-supervised text document clustering. An active learning approach is proposed to select informative document pairs for obtaining user feedbacks. A gain directed document pair selection method that measures how much we can learn by revealing the relationships between pairs of documents is designed. Three different models, namely, uncertainty model, generation error model, and objective function model are proposed. Language modeling is investigated for representing clusters in the semi-supervised document clustering approach.

This paper investigates active learning of constraints for semi-supervised document clustering. We make use of the intermediate clustering results to guide the document pair selection for obtaining user judgments for constraint generation. A gain function is designed for choosing the most informative document pairs given the current cluster assignments. This gain function measures how much we can learn by revealing the judgment of the document pairs. Two methods are investigated, namely, independent gain model and dependent gain model. In the independent gain model, we assume that the information learned by revealing the judgment of a document pair is independent of revealing the judgment of other document pairs. The dependent gain model also considers previously chosen documents to avoid redundant selection and maximize the gain collectively for a set of document pairs. Constrained semi-supervised clustering and gain directed document pair selection are conducted in an iterative manner. We have conducted extensive experiments on several real-world corpora. The results demonstrate that the intermediate clustering assignments and the interactions among a set of document pairs are useful for improving the clustering performance. Our approach is also superior to a recent existing work for this problem.

This article introduces a named entity matching model that makes use of both semantic and phonetic evidence. The matching of semantic and phonetic information is captured by a unified framework via a bipartite graph model. By considering various technical challenges of the problem, including order insensitivity and partial matching, this approach is less rigid than existing approaches and highly robust. One major component is a phonetic matching model which exploits similarity at the phoneme level. Two learning algorithms for learning the similarity information of basic phonemic matching units based on training examples are investigated. By applying the proposed named entity matching model, a mining system is developed for discovering new named entity translations from daily Web news. The system is able to discover new name translations that cannot be found in the existing bilingual dictionary.

No abstract available.

We propose a novel named entity matching model which considers both semantic and phonetic clues. The matching is formulated as an optimization problem. One major component is a phonetic matching model which exploits similarity at the phoneme level. We investigate three learning algorithms for obtaining the similarity information of basic phoneme units based on training examples. By applying this proposed named entity matching model, we also develop a mining framework for discovering new, unseen named entity translations from online daily Web news. This framework harvests comparable news in different languages using an existing bilingual dictionary. It is able to discover new name translations not found in the dictionary.