  Neural abstractive summarization has been studied in many pieces of
literature and achieves great success with the aid of large corpora. However,
when encountering novel tasks, one may not always benefit from transfer
learning due to the domain shifting problem, and overfitting could happen
without adequate labeled examples. Furthermore, the annotations of abstractive
summarization are costly, which often demand domain knowledge to ensure the
ground-truth quality. Thus, there are growing appeals for Low-Resource
Abstractive Summarization, which aims to leverage past experience to improve
the performance with limited labeled examples of target corpus. In this paper,
we propose to utilize two knowledge-rich sources to tackle this problem, which
are large pre-trained models and diverse existing corpora. The former can
provide the primary ability to tackle summarization tasks; the latter can help
discover common syntactic or semantic information to improve the generalization
ability. We conduct extensive experiments on various summarization corpora with
different writing styles and forms. The results demonstrate that our approach
achieves the state-of-the-art on 6 corpora in low-resource scenarios, with only
0.7% of trainable parameters compared to previous work.
