Multi-document summarization for low-resource languages faces a critical trade-off: large language models are computationally prohibitive for most institutions, while smaller models suffer from severe hallucination in abstractive generation. We address this through extractive sentence retrieval, which guarantees faithfulness while operating within constrained computational budgets. Our approach combines language-adaptive mixture-of-experts embeddings with graph neural networks that model discourse structure, addressing linguistic challenges across typologically diverse low-resource languages. With only 3.2M trainable parameters, our model requires 28 times less training time than comparable transformer-based approaches, making it practical for single-GPU environments typical in resource-limited settings. We demonstrate applicability to some main Southeast Asia (SEA) countries including Vietnam, Thailand, Laos, Indonesia, and Malaysia, representing three distinct language families: Austroasiatic, Kra-Dai, and Austronesian.

Case-to-Statute Retrieval—identifying applicable statutes from case facts—is essential for judicial efficiency but remains underexplored in low-resource languages like Vietnamese, where annotated legal corpora are scarce and domain-specific challenges persist. To advance research in this setting, we introduce ViCSR, a new benchmark of 10,000 Vietnamese criminal judgments and 1,122 statutory articles with citation-based relevance labels. We further propose a lightweight two-stage framework that performs efficient candidate retrieval with a fine-tuned Vietnamese bi-encoder and leverages citation structure for reranking with a compact heterogeneous GNN. Experiments show clear improvements over strong sparse and dense baselines, highlighting the importance of both domain adaptation and structure-aware modeling in low-resource legal IR. We will publicly release the benchmark, code, and checkpoints.

Counterfactual explanations (CEs) provide an intuitive way to understand recommender systems by identifying minimal modifications to user-item interactions that alter recommendation outcomes. Existing CE methods for recommender systems, however, have been evaluated under heterogeneous protocols, using different datasets, recommenders, metrics, and even explanation formats, which hampers reproducibility and fair comparison. Our paper systematically reproduces, re-implement, and re-evaluate eleven state-of-the-art CE methods for recommender systems, covering both native explainers (e.g., LIME-RS, SHAP, PRINCE, ACCENT, LXR, GREASE) and specific graph-based explainers originally proposed for GNNs. Here, a unified benchmarking framework is proposed to assess explainers along three dimensions: explanation format (implicit vs. explicit), evaluation level (item-level vs. list-level), and perturbation scope (user interaction vectors vs. user-item interaction graphs). Our evaluation protocol includes effectiveness, sparsity, and computational complexity metrics, and extends existing item-level assessments to top-K list-level explanations. Through extensive experiments on three real-world datasets and six representative recommender models, we analyze how well previously reported strengths of CE methods generalize across diverse setups. We observe that the trade-off between effectiveness and sparsity depends strongly on the specific method and evaluation setting, particularly under the explicit format; in addition, explainer performance remains largely consistent across item level and list level evaluations, and several graph-based explainers exhibit notable scalability limitations on large recommender graphs. Our results refine and challenge earlier conclusions about the robustness and practicality of CE generation methods in recommender systems: https://github.com/L2R-UET/CFExpRec.

Recommendation systems have faced significant challenges in cold-start scenarios, where new items with a limited history of interaction need to be effectively recommended to users. Though multimodal data (e.g., images, text, audio, etc.) offer rich information to address this issue, existing approaches often employ simplistic integration methods such as concatenation, average pooling, or fixed weighting schemes, which fail to capture the complex relationships between modalities. Our study proposes a novel Mixture of Experts framework for multimodal cold-start recommendation (MAMEX), which dynamically leverages latent representation from different modalities. MAMEX utilizes modality-specific expert networks and introduces a learnable gating mechanism that adaptively weights the contribution of each modality based on its content characteristics. This approach enables MAMEX to emphasize the most informative modalities for each item while maintaining robustness when certain modalities are less relevant or missing. Extensive experiments on benchmark datasets show that MAMEX outperforms state-of-the-art models with superior accuracy and adaptability.