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

Abstractive Summarization for Low Resource Data using Domain Transfer and Data Synthesis