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.