@inproceedings{jung-etal-2026-reader,
    title = "No Reader Left Behind: Multi-Agent Summaries Everyone Can Understand",
    author = "Jung, Jimin  and
      Kim, MyoungJin  and
      Seo, Jaehyung  and
      Lim, Heuiseok",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.2/",
    pages = "87--116",
    ISBN = "979-8-89176-390-6",
    abstract = "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."
}