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[2602.20040] AgenticSum: An Agentic Inference-Time Framework for Faithful Clinical Text Summarization
Summary
AgenticSum presents a novel framework for improving clinical text summarization using large language models, focusing on reducing factual inconsistencies and enhancing content reliability.
Why It Matters
This research addresses a critical challenge in clinical documentation, where maintaining factual accuracy is essential. By introducing an agentic framework that systematically improves summarization processes, it has the potential to enhance the reliability of automated clinical summaries, which can significantly impact patient care and medical decision-making.
Key Takeaways
- AgenticSum separates summarization into distinct stages: context selection, generation, verification, and correction.
- The framework shows consistent improvements over traditional large language models in clinical text summarization.
- Evaluation methods include reference-based metrics and human assessments, confirming the framework's effectiveness.
- Targeted correction reduces hallucinations and enhances factual consistency in generated summaries.
- The research contributes to the ongoing development of reliable AI tools for healthcare applications.
Computer Science > Computation and Language arXiv:2602.20040 (cs) [Submitted on 23 Feb 2026] Title:AgenticSum: An Agentic Inference-Time Framework for Faithful Clinical Text Summarization Authors:Fahmida Liza Piya, Rahmatollah Beheshti View a PDF of the paper titled AgenticSum: An Agentic Inference-Time Framework for Faithful Clinical Text Summarization, by Fahmida Liza Piya and 1 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) offer substantial promise for automating clinical text summarization, yet maintaining factual consistency remains challenging due to the length, noise, and heterogeneity of clinical documentation. We present AgenticSum, an inference-time, agentic framework that separates context selection, generation, verification, and targeted correction to reduce hallucinated content. The framework decomposes summarization into coordinated stages that compress task-relevant context, generate an initial draft, identify weakly supported spans using internal attention grounding signals, and selectively revise flagged content under supervisory control. We evaluate AgenticSum on two public datasets, using reference-based metrics, LLM-as-a-judge assessment, and human evaluation. Across various measures, AgenticSum demonstrates consistent improvements compared to vanilla LLMs and other strong baselines. Our results indicate that structured, agentic design with targeted correction offers an effective inference time solution to improve clin...