Computational notebooks, like Kaggle notebooks, offer an integrated platform for coding and documentation, yet the latter’s quality often falls short as scientists may neglect this crucial aspect. This paper addresses the need for improved and efficient code documentation generation in computational notebooks. As recent literature emphasizes integrating code’s inherent structure into documentation generation models, our research explores unutilized structural characteristics, incorporating metrics from code sequences to enable better code documentation suggestions. Evidenced by the improved BLEU scores, our proposed method significantly outperforms the conventional model in a preliminary 10-fold cross-validation experiment and further provides a flexible foundation for integrating source code metrics into diverse code documentation generation models.