Kaiwen Tan

Shengxiang Gao, Fang nan, Yongbing Zhang et al.

Existing research on news summarization primarily focuses on single-language single-document (SLSD), single-language multi-document (SLMD) or cross-language single-document (CLSD). However, in real-world scenarios, news about a international event often involves multiple documents in different languages, i.e., mixed-language multi-document (MLMD). Therefore, summarizing MLMD news is of great significance. However, the lack of datasets for MLMD news summarization has constrained the development of research in this area. To fill this gap, we construct a mixed-language multi-document news summarization dataset (MLMD-news), which contains four different languages and 10,992 source document cluster and target summary pairs. Additionally, we propose a graph-based extract-generate model and benchmark various methods on the MLMD-news dataset and publicly release our dataset and code\footnote[1]{https://github.com/Southnf9/MLMD-news}, aiming to advance research in summarization within MLMD scenarios.

Houyi Qi, Minghui Liwang, Kaiwen Tan et al.

Beyond data collection, future mobile crowdsensing (MCS) in complex applications must satisfy diverse requirements, including reliable task completion, budget and quality constraints, and fluctuating worker availability. Besides raw-data and location privacy, workers' intent/preference traces can be exploited by an honest-but-curious platform, enabling intent inference from repeated observations and frequency profiling. Meanwhile, worker dropouts and execution uncertainty may cause coverage instability and redundant sensing, while repeated global online re-optimization incurs high interaction overhead and enlarges the observable attack surface. To address these issues, we propose iParts, an intent-preserving and risk-controllable two-stage service provisioning framework for dynamic MCS. In the offline stage, workers report perturbed intent vectors via personalized local differential privacy with memorization/permanent randomization, suppressing frequency-based inference while preserving decision utility. Using only perturbed intents, the platform builds a redundancy-aware quality model and performs risk-aware pre-planning under budget, individual rationality, quality-failure risk, and intent-mismatch risk constraints. We formulate offline pre-planning as an exact potential game with expected social welfare as the potential function, ensuring a constrained pure-strategy Nash equilibrium and finite-step convergence under asynchronous feasible improvements. In the online stage, when runtime dynamics cause quality deficits, a temporary-recruitment potential game over idle/standby workers enables lightweight remediation with bounded interaction rounds and low observability. Experiments show that iParts achieves a favorable privacy-utility-efficiency trade-off, improving welfare and task completion while reducing redundancy and communication overhead compared with representative baselines.

Yongbing Zhang, Fang Nan, Shengxiang Gao et al.

The core challenge faced by multi-document summarization is the complexity of relationships among documents and the presence of information redundancy. Graph clustering is an effective paradigm for addressing this issue, as it models the complex relationships among documents using graph structures and reduces information redundancy through clustering, achieving significant research progress. However, existing methods often only consider single-relational graphs and require a predefined number of clusters, which hinders their ability to fully represent rich relational information and adaptively partition sentence groups to reduce redundancy. To overcome these limitations, we propose MRGSEM-Sum, an unsupervised multi-document summarization framework based on multi-relational graphs and structural entropy minimization. Specifically, we construct a multi-relational graph that integrates semantic and discourse relations between sentences, comprehensively modeling the intricate and dynamic connections among sentences across documents. We then apply a two-dimensional structural entropy minimization algorithm for clustering, automatically determining the optimal number of clusters and effectively organizing sentences into coherent groups. Finally, we introduce a position-aware compression mechanism to distill each cluster, generating concise and informative summaries. Extensive experiments on four benchmark datasets (Multi-News, DUC-2004, PubMed, and WikiSum) demonstrate that our approach consistently outperforms previous unsupervised methods and, in several cases, achieves performance comparable to supervised models and large language models. Human evaluation demonstrates that the summaries generated by MRGSEM-Sum exhibit high consistency and coverage, approaching human-level quality.

Kaiwen Tan, Weixian Huang, Xiaofeng Liu et al.

Morphological attributes from histopathological images and molecular profiles from genomic data are important information to drive diagnosis, prognosis, and therapy of cancers. By integrating these heterogeneous but complementary data, many multi-modal methods are proposed to study the complex mechanisms of cancers, and most of them achieve comparable or better results from previous single-modal methods. However, these multi-modal methods are restricted to a single task (e.g., survival analysis or grade classification), and thus neglect the correlation between different tasks. In this study, we present a multi-modal fusion framework based on multi-task correlation learning (MultiCoFusion) for survival analysis and cancer grade classification, which combines the power of multiple modalities and multiple tasks. Specifically, a pre-trained ResNet-152 and a sparse graph convolutional network (SGCN) are used to learn the representations of histopathological images and mRNA expression data respectively. Then these representations are fused by a fully connected neural network (FCNN), which is also a multi-task shared network. Finally, the results of survival analysis and cancer grade classification output simultaneously. The framework is trained by an alternate scheme. We systematically evaluate our framework using glioma datasets from The Cancer Genome Atlas (TCGA). Results demonstrate that MultiCoFusion learns better representations than traditional feature extraction methods. With the help of multi-task alternating learning, even simple multi-modal concatenation can achieve better performance than other deep learning and traditional methods. Multi-task learning can improve the performance of multiple tasks not just one of them, and it is effective in both single-modal and multi-modal data.