CiMaTe: Citation Count Prediction Effectively Leveraging the Main Text

• URL: http://arxiv.org/abs/2410.04404v1
• Date: Sun, 6 Oct 2024 08:39:13 GMT
• Title: CiMaTe: Citation Count Prediction Effectively Leveraging the Main Text
• Authors: Jun Hirako, Ryohei Sasano, Koichi Takeda,
• Abstract summary: Main text is an important factor for citation count prediction, but it is difficult to handle in machine learning models because the main text is typically very long. We propose a BERT-based citation count prediction model, called CiMaTe, that leverages the main text by explicitly capturing a paper's sectional structure.
• Score: 14.279848166377667
• License: http://creativecommons.org/licenses/by/4.0/
• 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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