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We consider the hierarchical representation of documents as graphs and usegeometric deep learning to classify them into different categories. While graphneural networks can efficiently handle the variable structure of hierarchicaldocuments using the permutation invariant message passing operations, we showthat we can gain extra performance improvements using our proposed selectivegraph pooling operation that arises from the fact that some parts of thehierarchy are invariable across different documents. We applied our model toclassify clinical trial (CT) protocols into completed and terminatedcategories. We use bag-of-words based, as well as pre-trained transformer-basedembeddings to featurize the graph nodes, achieving f1-scores around 0.85 on apublicly available large scale CT registry of around 360K protocols. We furtherdemonstrate how the selective pooling can add insights into the CT terminationstatus prediction. We make the source code and dataset splits accessible.

Classification of hierarchical text using geometric deep learning: the case of clinical trials corpus