# Retrieval Augmented Generation and Representative Vector Summarization for large unstructured textual data in Medical Education

arXiv: https://arxiv.org/abs/2308.00479 · HTML: https://mlsanity.com/paper/2308.00479/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in Medical and biomedical RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 3 of its 8 references that are in the map.
- First posted on arXiv 1 Aug 2023.

## Placed in

- Retrieval-augmented generation / [Medical and biomedical RAG](https://mlsanity.com/rag/medical-and-biomedical-rag/)

## Builds on (most cited first, 3 of 3)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)

## Built on by (most cited first, 10 of 10)

- [A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methods](https://mlsanity.com/paper/2403.02901/) (2024)
- [The Sound of Healthcare: Improving Medical Transcription ASR Accuracy with Large Language Models](https://mlsanity.com/paper/2402.07658/) (2024)
- [Multi-Head RAG: Solving Multi-Aspect Problems with LLMs](https://mlsanity.com/paper/2406.05085/) (2024)
- [Aligning Large Language Models for Clinical Tasks](https://mlsanity.com/paper/2309.02884/) (2023)
- [LawPal : A Retrieval Augmented Generation Based System for Enhanced Legal Accessibility in India](https://mlsanity.com/paper/2502.16573/) (2025)
- [Affordable AI Assistants with Knowledge Graph of Thoughts](https://mlsanity.com/paper/2504.02670/) (2025)
- [Empowering Meta-Analysis: Leveraging Large Language Models for Scientific Synthesis](https://mlsanity.com/paper/2411.10878/) (2024)
- [QACP: An Annotated Question Answering Dataset for Assisting Chinese Python Programming Learners](https://mlsanity.com/paper/2402.07913/) (2024)
- [GraphSeek: Next-Generation Graph Analytics with LLMs](https://mlsanity.com/paper/2602.11052/) (2026)
- [Context Matters: Pushing the Boundaries of Open-Ended Answer Generation with Graph-Structured Knowledge Context](https://mlsanity.com/paper/2401.12671/) (2024)

## Abstract (arXiv)

Large Language Models are increasingly being used for various tasks including content generation and as chatbots. Despite their impressive performances in general tasks, LLMs need to be aligned when applying for domain specific tasks to mitigate the problems of hallucination and producing harmful answers. Retrieval Augmented Generation (RAG) allows to easily attach and manipulate a non-parametric knowledgebases to LLMs. Applications of RAG in the field of medical education are discussed in this paper. A combined extractive and abstractive summarization method for large unstructured textual data using representative vectors is proposed.