  Jupyter notebook allows data scientists to write machine learning code
together with its documentation in cells. In this paper, we propose a new task
of code documentation generation (CDG) for computational notebooks. In contrast
to the previous CDG tasks which focus on generating documentation for single
code snippets, in a computational notebook, one documentation in a markdown
cell often corresponds to multiple code cells, and these code cells have an
inherent structure. We proposed a new model (HAConvGNN) that uses a
hierarchical attention mechanism to consider the relevant code cells and the
relevant code tokens information when generating the documentation. Tested on a
new corpus constructed from well-documented Kaggle notebooks, we show that our
model outperforms other baseline models.
