@inproceedings{cimino-etal-2026-clarus,
title = "{CLARUS}: Hierarchical Hypergraph Modeling for Unsupervised Discharge Summarization",
author = "Cimino, Gaetano and
Hossain, Mir Rayat Imtiaz and
De Santis, Laura and
Abaskohi, Amirhossein and
Ng, Raymond T. and
Carenini, Giuseppe and
Deufemia, Vincenzo",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2067/",
pages = "47070--47091",
ISBN = "979-8-89176-459-0",
abstract = "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."
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T CLARUS: Hierarchical Hypergraph Modeling for Unsupervised Discharge Summarization
%A Cimino, Gaetano
%A Hossain, Mir Rayat Imtiaz
%A De Santis, Laura
%A Abaskohi, Amirhossein
%A Ng, Raymond T.
%A Carenini, Giuseppe
%A Deufemia, Vincenzo
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F cimino-etal-2026-clarus
%X 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.
%U https://aclanthology.org/2026.emnlp-main.2067/
%P 47070-47091
Markdown (Informal)
[CLARUS: Hierarchical Hypergraph Modeling for Unsupervised Discharge Summarization](https://aclanthology.org/2026.emnlp-main.2067/) (Cimino et al., EMNLP 2026)
ACL