mlsanityData to 14 Oct 2026

Biomedical Question Answering via Multi-Level Summarization on a Local Knowledge Graph

arXiv 2504.01309 ↗First posted 2 Apr 2025cs.CL cs.AI cs.IRcited by 0

On the map: Retrieval-augmented generation → Medical and biomedical RAG

Abstract (arXiv)

In Question Answering (QA), Retrieval Augmented Generation (RAG) has revolutionized performance in various domains. However, how to effectively capture multi-document relationships, particularly critical for biomedical tasks, remains an open question. In this work, we propose a novel method that utilizes propositional claims to construct a local knowledge graph from retrieved documents. Summaries are then derived via layerwise summarization from the knowledge graph to contextualize a small language model to perform QA. We achieved comparable or superior performance with our method over RAG baselines on several biomedical QA benchmarks. We also evaluated each individual step of our methodology over a targeted set of metrics, demonstrating its effectiveness.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical and biomedical RAG, one of 15 directions in Retrieval-augmented generation. Builds on 16 of its 25 references that are in the map. First posted on arXiv 2 Apr 2025.

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16 of 25 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024BiomedRAG: A Retrieval Augmented Large Language Model for Biomedicine1 May
  2. 2024Benchmarking Retrieval-Augmented Generation for Medicine20 Feb

Other references · 14

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2020Measuring Massive Multitask Language Understandingcited by 7,963
  3. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  4. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,453
  5. 2020What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Examscited by 1,356
  6. 2019PubMedQA: A Dataset for Biomedical Research Question Answeringcited by 1,288
  7. 2024RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrievalcited by 532
  8. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 517
  9. 2019ScispaCy: Fast and Robust Models for Biomedical Natural Language Processingcited by 244
  10. 2023Knowledge Graph Prompting for Multi-Document Question Answeringcited by 152
  11. 2023A Survey of Large Language Models in Medicine: Progress, Application, and Challengecited by 124
  12. 2024From Isolated Conversations to Hierarchical Schemas: Dynamic Tree Memory Representation for LLMscited by 79
2 more
  1. 2023Llama2Vec: Unsupervised Adaptation of Large Language Models for Dense Retrievalcited by 78
  2. 2024Harnessing the Power of Semi-Structured Knowledge and LLMs with Triplet-Based Prefiltering for Question Answeringcited by 4

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  1. 2026Adaptive Retrieval Strategies for Biomedical Question Answering
  2. 2025Agentic Medical Knowledge Graphs Enhance Medical Question Answering: Bridging the Gap Between LLMs and Evolving Medical Knowledge
  3. 2025RAG-BioQA: A Retrieval-Augmented Generation Framework for Long-Form Biomedical Question Answering
  4. 2025Efficient and Reproducible Biomedical Question Answering using Retrieval Augmented Generation
  5. 2025Talk Before You Retrieve: Agent-Led Discussions for Better RAG in Medical QA
  6. 2024Question-Answering Based Summarization of Electronic Health Records using Retrieval Augmented Generation
  7. 2023MKRAG: Medical Knowledge Retrieval Augmented Generation for Medical Question Answering
  8. 2023Biomedical knowledge graph-optimized prompt generation for large language models