# ACM-CR: A Manually Annotated Test Collection for Citation Recommendation

arXiv: https://arxiv.org/abs/2108.07571 · HTML: https://mlsanity.com/paper/2108.07571/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 9 references that are in the map.
- First posted on arXiv 17 Aug 2021.

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

- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Citation Recommendation: Approaches and Datasets](https://mlsanity.com/paper/2002.06961/) (2020)
- [HybridCite: A Hybrid Model for Context-Aware Citation Recommendation](https://mlsanity.com/paper/2002.06406/) (2020)

## Built on by (most cited first, 1 of 1)

- [ERU-KG: Efficient Reference-aligned Unsupervised Keyphrase Generation](https://mlsanity.com/paper/2505.24219/) (2025)

## Abstract (arXiv)

Citation recommendation is intended to assist researchers in the process of searching for relevant papers to cite by recommending appropriate citations for a given input text. Existing test collections for this task are noisy and unreliable since they are built automatically from parsed PDF papers. In this paper, we present our ongoing effort at creating a publicly available, manually annotated test collection for citation recommendation. We also conduct a series of experiments to evaluate the effectiveness of content-based baseline models on the test collection, providing results for future work to improve upon. Our test collection and code to replicate experiments are available at https://github.com/boudinfl/acm-cr