  We consider the hierarchical representation of documents as graphs and use
geometric deep learning to classify them into different categories. While graph
neural networks can efficiently handle the variable structure of hierarchical
documents using the permutation invariant message passing operations, we show
that we can gain extra performance improvements using our proposed selective
graph pooling operation that arises from the fact that some parts of the
hierarchy are invariable across different documents. We applied our model to
classify clinical trial (CT) protocols into completed and terminated
categories. We use bag-of-words based, as well as pre-trained transformer-based
embeddings to featurize the graph nodes, achieving f1-scores around 0.85 on a
publicly available large scale CT registry of around 360K protocols. We further
demonstrate how the selective pooling can add insights into the CT termination
status prediction. We make the source code and dataset splits accessible.
