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 …