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

Source: [Biomedical Question Answering via Multi-Level Summarization on a Local Knowledge Graph](<https://www.sota2.com/research/paper/68fa77c61e209131cea753ec>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T04:26:29.946Z

Authors: Lingxiao Guan, Yuanhao Huang, Jie Liu

Published: 2025-04-02T02:40:19.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2504.01309>)

[PDF](<https://arxiv.org/pdf/2504.01309.pdf>)