  Although domain shift has been well explored in many NLP applications, it
still has received little attention in the domain of extractive text
summarization. As a result, the model is under-utilizing the nature of the
training data due to ignoring the difference in the distribution of training
sets and shows poor generalization on the unseen domain.
  With the above limitation in mind, in this paper, we first extend the
conventional definition of the domain from categories into data sources for the
text summarization task. Then we re-purpose a multi-domain summarization
dataset and verify how the gap between different domains influences the
performance of neural summarization models.
  Furthermore, we investigate four learning strategies and examine their
abilities to deal with the domain shift problem.
  Experimental results on three different settings show their different
characteristics in our new testbed.
  Our source code including \textit{BERT-based}, \textit{meta-learning} methods
for multi-domain summarization learning and the re-purposed dataset
\textsc{Multi-SUM} will be available on our project:
\url{http://pfliu.com/TransferSum/}.
