No Reader Left Behind: Multi-Agent Summaries Everyone Can Understand
Quick Take
The NRLB framework enhances plain language summarization for diverse readers, improving readability by 55% to 76% while maintaining factual accuracy. It addresses barriers faced by elementary students, non-native speakers, and those with attention deficits through a multi-agent approach.
Key Points
- NRLB simulates three reader groups: elementary students, non-native speakers, and attention deficit readers.
- Combines template-based planning with iterative refinement for better understanding.
- Achieves consistent readability improvements across multiple datasets.
- Human evaluations show a preference rate between 55% and 76% for NRLB summaries.
- Focuses on resolving difficult terms and confusing sentences for broader accessibility.
Article Excerpt
From source RSS / original summaryarXiv:2605. 28836v1 Announce Type: new Abstract: 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.
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