  Deep neural networks are data hungry models and thus face difficulties when
attempting to train on small text datasets. Transfer learning is a potential
solution but their effectiveness in the text domain is not as explored as in
areas such as image analysis. In this paper, we study the problem of transfer
learning for text summarization and discuss why existing state-of-the-art
models fail to generalize well on other (unseen) datasets. We propose a
reinforcement learning framework based on a self-critic policy gradient
approach which achieves good generalization and state-of-the-art results on a
variety of datasets. Through an extensive set of experiments, we also show the
ability of our proposed framework to fine-tune the text summarization model
using only a few training samples. To the best of our knowledge, this is the
first work that studies transfer learning in text summarization and provides a
generic solution that works well on unseen data.
