  Sentence insertion is a delicate but fundamental NLP problem. Current
approaches in sentence ordering, text coherence, and question answering (QA)
are neither suitable nor good at solving it. In this paper, We propose
InsertGNN, a simple yet effective model that represents the problem as a graph
and adopts the graph Neural Network (GNN) to learn the connection between
sentences. It is also supervised by both the local and global information that
the local interactions of neighboring sentences can be considered. To the best
of our knowledge, this is the first recorded attempt to apply a supervised
graph-structured model in sentence insertion. We evaluate our method in our
newly collected TOEFL dataset and further verify its effectiveness on the
larger arXivdataset using cross-domain learning. The experiments show that
InsertGNN outperforms the unsupervised text coherence method, the topological
sentence ordering approach, and the QA architecture. Specifically, It achieves
an accuracy of 70%, rivaling the average human test scores.
