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

Understand

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.