CiMaTe: Citation Count Prediction Effectively Leveraging the Main Text
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866916424678637568 |
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| author | Hirako, Jun Sasano, Ryohei Takeda, Koichi |
| author_facet | Hirako, Jun Sasano, Ryohei Takeda, Koichi |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_04404 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CiMaTe: Citation Count Prediction Effectively Leveraging the Main Text Hirako, Jun Sasano, Ryohei Takeda, Koichi Computation and Language 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. |
| title | CiMaTe: Citation Count Prediction Effectively Leveraging the Main Text |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2410.04404 |