2024/10/06 by Jun Hirako, Hirako, Jun, Ryohei Sasano +3 · 1 citation
Computer Science · Decision Sciences · #Advanced Text Analysis Techniques #Computation and Language (cs.CL) #Data Quality and Management #FOS: Computer and information sciences #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2410.04404
openalex publication_date 2024/10/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.