Configurable Semantic Chunking for Biomedical Information Extraction in Retrieval-Augmented Generation

Paper recorded by Signals 4 on 2026-08-31 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-08-31 on arXiv · recorded by Signals 4 on 2026-09-01

Category: cs.CL · 自然语言处理 · first seen 2026-09-01

Abstract

BioMedRAG introduced retrieval-augmented generation with a learned chunk scorer for biomedical information extraction. However, it relies on fixed-size chunking which can fragment semantic evidence. We propose a configurable semantic chunking framework that addresses this limitation by combining entity-preserving windows, trigger-centered chunking, proposition-first extraction, tiered trigger prio

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