No Reader Left Behind: Multi-Agent Summaries Everyone Can Understand
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Generating Topic-Oriented Summaries Using Neural Attention
Summarizing a document requires identifying the important parts of the document with an objective of providing a quick overview to a reader. However, a long article can span several topics and a single summary cannot do …
Abstractive Text SummarizationText SummarizationReader-Aware Multi-Document Summarization via Sparse Coding
We propose a new MDS paradigm called reader-aware multi-document summarization (RA-MDS). Specifically, a set of reader comments associated with the news reports are also collected. The generated summaries from the report…
Document SummarizationMulti-Document SummarizationReader-Aware SummarizationFairness for Whom? Understanding the Reader's Perception of Fairness in Text Summarization
With the surge in user-generated textual information, there has been a recent increase in the use of summarization algorithms for providing an overview of the extensive content. Traditional metrics for evaluation of thes…
FairnessText SummarizationPaper Plain: Making Medical Research Papers Approachable to Healthcare Consumers with Natural Language Processing
When seeking information not covered in patient-friendly documents, like medical pamphlets, healthcare consumers may turn to the research literature. Reading medical papers, however, can be a challenging experience. To i…
What's the Issue Here?: Task-based Evaluation of Reader Comment Summarization Systems
Automatic summarization of reader comments in on-line news is an extremely challenging task and a capability for which there is a clear need. Work to date has focussed on producing extractive summaries using well-known t…
Clustering