CiMaTe：有效利用正文的引用量预测方法
CiMaTe: Citation Count Prediction Effectively Leveraging the Main Text
AI总结：
针对现有引用量预测未充分利用长正文信息的问题，提出基于BERT、显式利用论文章节结构的预测模型CiMaTe，在两个领域的Spearman秩相关系数上均优于此前方法。
摘要：
在论文数量持续增长的背景下，预测论文未来引用量对于筛选有价值的论文愈发重要。尽管论文正文是引用量预测的关键因素，但由于正文通常篇幅很长，机器学习模型难以处理，因此以往研究尚未充分探索如何利用正文信息。本文提出一种基于BERT的引用量预测模型CiMaTe，该模型通过显式捕捉论文的章节结构来利用正文信息。通过在计算语言学和生物学领域论文上的实验，我们验证了CiMaTe的有效性，其在Spearman秩相关系数上优于现有方法：在计算语言学领域提升5.1个点，在生物学领域提升1.8个点。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
