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Jupyter notebook allows data scientists to write machine learning codetogether with its documentation in cells. In this paper, we propose a new taskof code documentation generation (CDG) for computational notebooks. In contrastto the previous CDG tasks which focus on generating documentation for singlecode snippets, in a computational notebook, one documentation in a markdowncell often corresponds to multiple code cells, and these code cells have aninherent structure. We proposed a new model (HAConvGNN) that uses ahierarchical attention mechanism to consider the relevant code cells and therelevant code tokens information when generating the documentation. Tested on anew corpus constructed from well-documented Kaggle notebooks, we show that ourmodel outperforms other baseline models.

HAConvGNN: Hierarchical Attention Based Convolutional Graph Neural Network for Code Documentation Generation in Jupyter Notebooks