  In this paper, we take stock of the current state of summarization datasets
and explore how different factors of datasets influence the generalization
behaviour of neural extractive summarization models. Specifically, we first
propose several properties of datasets, which matter for the generalization of
summarization models. Then we build the connection between priors residing in
datasets and model designs, analyzing how different properties of datasets
influence the choices of model structure design and training methods. Finally,
by taking a typical dataset as an example, we rethink the process of the model
design based on the experience of the above analysis. We demonstrate that when
we have a deep understanding of the characteristics of datasets, a simple
approach can bring significant improvements to the existing state-of-the-art
model.A
