  Training abstractive summarization models typically requires large amounts of
data, which can be a limitation for many domains. In this paper we explore
using domain transfer and data synthesis to improve the performance of recent
abstractive summarization methods when applied to small corpora of student
reflections. First, we explored whether tuning state of the art model trained
on newspaper data could boost performance on student reflection data.
Evaluations demonstrated that summaries produced by the tuned model achieved
higher ROUGE scores compared to model trained on just student reflection data
or just newspaper data. The tuned model also achieved higher scores compared to
extractive summarization baselines, and additionally was judged to produce more
coherent and readable summaries in human evaluations. Second, we explored
whether synthesizing summaries of student data could additionally boost
performance. We proposed a template-based model to synthesize new data, which
when incorporated into training further increased ROUGE scores. Finally, we
showed that combining data synthesis with domain transfer achieved higher ROUGE
scores compared to only using one of the two approaches.
