  Many data scientists use Jupyter notebook to experiment code, visualize
results, and document rationales or interpretations. The code documentation
generation CDG task in notebooks is related but different from the code
summarization task in software engineering, as one documentation (markdown
cell) may consist of a text (informative summary or indicative rationale) for
multiple code cells. Our work aims to solve the CDG task by encoding the
multiple code cells as separated AST graph structures, for which we propose a
hierarchical attention-based ConvGNN component to augment the Seq2Seq network.
We build a dataset with publicly available Kaggle notebooks and evaluate our
model (HAConvGNN) against baseline models (e.g., Code2Seq or Graph2Seq).
