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arXiv · 2410.04404

CiMaTe: Citation Count Prediction Effectively Leveraging the Main Text

Jun Hirako·Ryohei Sasano·Koichi Takeda
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Abstract

Prediction of the future citation counts of papers is increasingly important to find interesting papers among an ever-growing number of papers. Although a paper's main text is an important factor for citation count prediction, it is difficult to handle in machine learning models because the main text is typically very long; thus previous studies have not fully explored how to leverage it. In this paper, we propose a BERT-based citation count prediction model, called CiMaTe, that leverages the main text by explicitly capturing a paper's sectional structure. Through experiments with papers from computational linguistics and biology domains, we demonstrate the CiMaTe's effectiveness, outperforming the previous methods in Spearman's rank correlation coefficient; 5.1 points in the computational linguistics domain and 1.8 points in the biology domain.

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Jun Hirako, Ryohei Sasano, Koichi Takeda. 2024-10-06. CiMaTe: Citation Count Prediction Effectively Leveraging the Main Text. https://arxiv.org/abs/2410.04404

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