# Automated Literature Review Using NLP Techniques and LLM-Based Retrieval-Augmented Generation

arXiv: https://arxiv.org/abs/2411.18583 · HTML: https://mlsanity.com/paper/2411.18583/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation.
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 27 Nov 2024.

## Placed in

- Retrieval-augmented generation / [RAG pipeline design and evaluation](https://mlsanity.com/rag/rag-pipeline-design-and-evaluation/)

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

- [TLDR: Extreme Summarization of Scientific Documents](https://mlsanity.com/paper/2004.15011/) (2020)
- [Can We Automate Scientific Reviewing?](https://mlsanity.com/paper/2102.00176/) (2021)
- [Automating Research Synthesis with Domain-Specific Large Language Model Fine-Tuning](https://mlsanity.com/paper/2404.08680/) (2024)

## Built on by (most cited first, 8 of 8)

- [Towards Robust Agentic CUDA Kernel Benchmarking, Verification, and Optimization](https://mlsanity.com/paper/2509.14279/) (2025)
- [How Far Are AI Scientists from Changing the World?](https://mlsanity.com/paper/2507.23276/) (2025)
- [SurveyGen-I: Consistent Scientific Survey Generation with Evolving Plans and Memory-Guided Writing](https://mlsanity.com/paper/2508.14317/) (2025)
- [SGSimEval: A Comprehensive Multifaceted and Similarity-Enhanced Benchmark for Automatic Survey Generation Systems](https://mlsanity.com/paper/2508.11310/) (2025)
- [Evaluating Prompting Strategies and Large Language Models in Systematic Literature Review Screening: Relevance and Task-Stage Classification](https://mlsanity.com/paper/2510.16091/) (2025)
- [From Facts to Folklore: Evaluating Large Language Models on Bengali Cultural Knowledge](https://mlsanity.com/paper/2510.20043/) (2025)
- [CEKER: A Generalizable LLM Framework for Literature Analysis with a Case Study in Unikernel Security](https://mlsanity.com/paper/2412.10904/) (2024)
- [HySemRAG: A Hybrid Semantic Retrieval-Augmented Generation Framework for Automated Literature Synthesis and Methodological Gap Analysis](https://mlsanity.com/paper/2508.05666/) (2025)

## Abstract (arXiv)

This research presents and compares multiple approaches to automate the generation of literature reviews using several Natural Language Processing (NLP) techniques and retrieval-augmented generation (RAG) with a Large Language Model (LLM). The ever-increasing number of research articles provides a huge challenge for manual literature review. It has resulted in an increased demand for automation. Developing a system capable of automatically generating the literature reviews from only the PDF files as input is the primary objective of this research work. The effectiveness of several Natural Language Processing (NLP) strategies, such as the frequency-based method (spaCy), the transformer model (Simple T5), and retrieval-augmented generation (RAG) with Large Language Model (GPT-3.5-turbo), is evaluated to meet the primary objective. The SciTLDR dataset is chosen for this research experiment and three distinct techniques are utilized to implement three different systems for auto-generating the literature reviews. The ROUGE scores are used for the evaluation of all three systems. Based on the evaluation, the Large Language Model GPT-3.5-turbo achieved the highest ROUGE-1 score, 0.364. The transformer model comes in second place and spaCy is at the last position. Finally, a graphical user interface is created for the best system based on the large language model.