# Simplifying Impact Prediction for Scientific Articles

arXiv: https://arxiv.org/abs/2012.15192 · HTML: https://mlsanity.com/paper/2012.15192/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2020.

## Built on by (most cited first, 2 of 2)

- [Navigating Through Paper Flood: Advancing LLM-based Paper Evaluation through Domain-Aware Retrieval and Latent Reasoning](https://mlsanity.com/paper/2508.05129/) (2025)
- [Comprehensive Manuscript Assessment with Text Summarization Using 69707 articles](https://mlsanity.com/paper/2503.20835/) (2025)

## Abstract (arXiv)

Estimating the expected impact of an article is valuable for various applications (e.g., article/cooperator recommendation). Most existing approaches attempt to predict the exact number of citations each article will receive in the near future, however this is a difficult regression analysis problem. Moreover, most approaches rely on the existence of rich metadata for each article, a requirement that cannot be adequately fulfilled for a large number of them. In this work, we take advantage of the fact that solving a simpler machine learning problem, that of classifying articles based on their expected impact, is adequate for many real world applications and we propose a simplified model that can be trained using minimal article metadata. Finally, we examine various configurations of this model and evaluate their effectiveness in solving the aforementioned classification problem.