  Neural network-based models augmented with unsupervised pre-trained knowledge
have achieved impressive performance on text summarization. However, most
existing evaluation methods are limited to an in-domain setting, where
summarizers are trained and evaluated on the same dataset. We argue that this
approach can narrow our understanding of the generalization ability for
different summarization systems. In this paper, we perform an in-depth analysis
of characteristics of different datasets and investigate the performance of
different summarization models under a cross-dataset setting, in which a
summarizer trained on one corpus will be evaluated on a range of out-of-domain
corpora. A comprehensive study of 11 representative summarization systems on 5
datasets from different domains reveals the effect of model architectures and
generation ways (i.e. abstractive and extractive) on model generalization
ability. Further, experimental results shed light on the limitations of
existing summarizers. Brief introduction and supplementary code can be found in
https://github.com/zide05/CDEvalSumm.
