  Sentence insertion is an interesting NLP problem but received insufficient
attention. Existing approaches in sentence ordering, text coherence, and
question answering are neither suitable nor good enough at solving it. To
bridge this gap, we propose InsertGNN, a simple yet effective model that
represents the problem as a graph and adopts a hierarchical graph neural
network (GNN) to learn the connection between sentences. We evaluate our method
in our newly collected TOEFL dataset and further verify its effectiveness on
the larger arXiv dataset using cross-domain learning. Extensive experiments
demonstrate that InsertGNN outperforms all baselines by a large margin with an
accuracy of 70\%, rivaling the average human test scores.
