Discharge summary generation from clinical records is challenging due to long, heterogeneous inputs and the need to preserve clinically relevant information. Although Large Language Models (LLMs) have improved abstractive summarization, their direct application to full patient records is limited by context length and difficulties in aggregating evidence across notes. We present CLARUS, a fully unsupervised framework that models patient records as hypergraphs encoding multi-dimensional relations. A hierarchical hypergraph encoder with depth-aware concept attention strengthens sparse representations, while a variational information bottleneck with Gumbel-Softmax relaxation enables differentiable selection of compact, informative subsets for LLM generation. Experiments on MIMIC-III show that CLARUS outperforms strong unsupervised baselines across lexical, semantic, and clinical metrics, demonstrating the value of higher-order structure and concept hierarchy for faithful multi-document clinical summarization.

Annotating discourse structure in multi-party dialogues is a cognitively demanding task that requires specialized expertise, yet existing annotation tools are either fully manual or provide only limited assistance. We present MUDIPA, a web-based, human-in-the-loop platform for discourse graph annotation in multi-party dialogues, built around two pluggable AI backends: a suggestion engine that produces confidence-ranked arc proposals, and an LLM-based reasoning engine that generates on-demand natural-language rationales for candidate attachments and relation labels. In a counterbalanced, within-subjects study with eight annotators, we compare AI-assisted annotation with MUDIPA against manual annotation. The AI-assisted condition achieves higher inter-annotator agreement on relation labels, higher accuracy against the gold standard, and lower perceived workload, while requiring only a modest increase in annotation time. These findings indicate that the principal value of LLMs in discourse annotation lies in supporting human decision-making, improving both annotation quality and inter-annotator agreement relative to unassisted annotation.