# An Artificial Intelligence Driven Semantic Similarity-Based Pipeline for Rapid Literature

arXiv: https://arxiv.org/abs/2509.15292 · HTML: https://mlsanity.com/paper/2509.15292/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 18 Sep 2025.

## Builds on (most cited first, 10 of 10)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering](https://mlsanity.com/paper/2007.01282/) (2020)
- [SPECTER: Document-level Representation Learning using Citation-informed Transformers](https://mlsanity.com/paper/2004.07180/) (2020)
- [SciRepEval: A Multi-Format Benchmark for Scientific Document Representations](https://mlsanity.com/paper/2211.13308/) (2022)
- [Can ChatGPT Write a Good Boolean Query for Systematic Review Literature Search?](https://mlsanity.com/paper/2302.03495/) (2023)
- [ChatCite: LLM Agent with Human Workflow Guidance for Comparative Literature Summary](https://mlsanity.com/paper/2403.02574/) (2024)
- [SciReviewGen: A Large-scale Dataset for Automatic Literature Review Generation](https://mlsanity.com/paper/2305.15186/) (2023)
- [Large Language Models for Automated Literature Review: An Evaluation of Reference Generation, Abstract Writing, and Review Composition](https://mlsanity.com/paper/2412.13612/) (2024)
- [SWARM-SLR -- Streamlined Workflow Automation for Machine-actionable Systematic Literature Reviews](https://mlsanity.com/paper/2407.18657/) (2024)

## Abstract (arXiv)

We propose an automated pipeline for performing literature reviews using semantic similarity. Unlike traditional systematic review systems or optimization based methods, this work emphasizes minimal overhead and high relevance by using transformer based embeddings and cosine similarity. By providing a paper title and abstract, it generates relevant keywords, fetches relevant papers from open access repository, and ranks them based on their semantic closeness to the input. Three embedding models were evaluated. A statistical thresholding approach is then applied to filter relevant papers, enabling an effective literature review pipeline. Despite the absence of heuristic feedback or ground truth relevance labels, the proposed system shows promise as a scalable and practical tool for preliminary research and exploratory analysis.