# Academic information retrieval using citation clusters: In-depth evaluation based on systematic reviews

arXiv: https://arxiv.org/abs/2207.03299 · HTML: https://mlsanity.com/paper/2207.03299/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2022.

## Built on by (most cited first, 3 of 3)

- [Semantically Orthogonal Framework for Citation Classification: Disentangling Intent and Content](https://mlsanity.com/paper/2601.05103/) (2026)
- [Submitted and Diagnostic Analysis of Full-Text Temporal Retrieval for LongEval-Sci](https://mlsanity.com/paper/2607.04088/) (2026)
- [Scientific Knowledge Discovery in the Age of Large Language Models](https://mlsanity.com/paper/2607.26670/) (2026)

## Abstract (arXiv)

The field of scientometrics has shown the power of citation-based clusters for literature analysis, yet this technique has barely been used for information retrieval tasks. This work evaluates the performance of citation based-clusters for information retrieval tasks. We simulated a search process using these clusters with a tree hierarchy of clusters and a cluster selection algorithm. We evaluated the task of finding the relevant documents for 25 systematic reviews. Our evaluation considered several trade-offs between recall and precision for the cluster selection, and we also replicated the Boolean queries self-reported by the systematic review to serve as a reference. We found that citation-based clusters search performance is highly variable and unpredictable, that it works best for users that prefer recall over precision at a ratio between 2 and 8, and that when used along with query-based search they complement each other, including finding new relevant documents.