The Plain Writing Act in the United States requires government documents to be written in clear and simple language.However, existing summarization systems struggle to address diverse linguistic and cognitive barriers among general readers.We propose NRLB (No Reader Left Behind), a unified multi-agent framework for plain language summarization that simulates three representative reader groups: elementary school students, non-native speakers, and readers with attention deficits.NRLB integrates template-based planning with an iterative feedback loop guided by simulated readers and domain expert revision to address comprehension barriers such as unknown terms, missing contexts, and confusing sentences.Evaluations across multiple datasets demonstrate consistent improvements in both readability and factuality.Human evaluation further supports these findings, with annotator preference rates ranging from 55% to 76%, highlighting NRLB's ability to generate summaries that are both faithful to the source and accessible to a wide range of readers.

Weihua Zheng, Zhengyuan Liu, Tanmoy Chakraborty, Weiwen Xu, Xiaoxue Gao, Bryan Chen Zhengyu Tan, Bowei Zou, Chang Liu, Yujia Hu, Xing Xie, Xiaoyuan Yi, Jing Yao, Chaojun Wang, Long Li, Rui Liu, Huiyao Liu, Koji Inoue, Ryuichi Sumida, Tatsuya Kawahara, Fan Xu, Lingyu Ye, Wei Tian, Dongjun Kim, Jimin Jung, Jaehyung Seo, Nadya Yuki Wangsajaya, Pham Minh Duc, Ojasva Saxena, Palash Nandi, Xiyan Tao, Wiwik Karlina, Tuan Luong, Keertana Arun Vasan, Roy Ka-Wei Lee, Nancy F. Chen. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

• Propose CenterIR, a cluster-aware regularizer for imbalanced EEG regression • Enhance depression severity estimation from resting-state EEG in older adults • Demonstrate stable performance gains over state-of-the-art baselines • Validate robustness through λ -sensitivity and loss ablation analyses Electroencephalography (EEG)-based mental health assessment has gained increasing attention as a non-invasive tool for quantifying depression severity in older adults. However, regression models for continuous severity prediction remain limited, particularly under imbalanced data distributions. This study presents a deep learning framework that integrates convolutional neural networks and bidirectional long short-term memory modules with a novel CenterIR loss to enhance regression performance. Resting-state EEG was recorded from 104 older adults under eyes-open (EO) and eyes-closed (EC) conditions. The proposed model outperformed baseline and state-of-the-art approaches, achieving an MSE of 0.151, MAE of 0.231, and R² of 0.990 in EO, and 0.270, 0.291, and 0.983, respectively, in EC. Paired t-test results indicated significantly better performance in the EO condition, highlighting the potential of EO resting EEG as a reliable neural marker for depression severity. Overall, our framework demonstrates effective imbalanced regression modeling for EEG-based depression assessment in older adults and provides insights for clinical application in mental health monitoring.