No Reader Left Behind: Multi-Agent Summaries Everyone Can Understand
For the general public and government agencies, NRLB addresses the need for accessible plain language summaries, but the improvements are incremental over existing methods.
NRLB is a multi-agent framework for plain language summarization that simulates three reader groups (elementary students, non-native readers, and readers with attention deficits) to improve readability while preserving factual accuracy. Human evaluation shows annotator preference rates of 55% to 76% over baselines.
The Plain Writing Act in the United States requires government documents to be accessible in clear and simple language that the general public can easily understand, yet existing summarization systems struggle to address diverse linguistic and cognitive barriers among general readers. We present NRLB (No Reader Left Behind), a multi-agent framework for plain language summarization that simulates three representative reader groups: elementary school student readers, non-native readers, and readers with attention deficits. NRLB combines template-based planning with iterative, reader-oriented refinement, enabling systematic detection and resolution of difficult terms, missing contexts, and confusing sentences. Evaluations across multiple datasets demonstrate consistent improvements in readability while preserving factual accuracy. Human evaluation further validates NRLB's impact, with annotator preference rates ranging from 55% to 76%, highlighting NRLB's potential to produce plain language summaries that are both faithful to the source and broadly accessible to the general public.