  This paper explores three simple data manipulation techniques (synthesis,
augmentation, curriculum) for improving abstractive summarization models
without the need for any additional data. We introduce a method of data
synthesis with paraphrasing, a data augmentation technique with sample mixing,
and curriculum learning with two new difficulty metrics based on specificity
and abstractiveness. We conduct experiments to show that these three techniques
can help improve abstractive summarization across two summarization models and
two different small datasets. Furthermore, we show that these techniques can
improve performance when applied in isolation and when combined.
