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

arXiv: https://arxiv.org/abs/2002.03407 · HTML: https://mlsanity.com/paper/2002.03407/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2020.

## Built on by (most cited first, 8 of 8)

- [AdaptSum: Towards Low-Resource Domain Adaptation for Abstractive Summarization](https://mlsanity.com/paper/2103.11332/) (2021)
- [Long Document Summarization in a Low Resource Setting using Pretrained Language Models](https://mlsanity.com/paper/2103.00751/) (2021)
- [Domain-Oriented Prefix-Tuning: Towards Efficient and Generalizable Fine-tuning for Zero-Shot Dialogue Summarization](https://mlsanity.com/paper/2204.04362/) (2022)
- [Exploring Multitask Learning for Low-Resource AbstractiveSummarization](https://mlsanity.com/paper/2109.08565/) (2021)
- [Automatic Document Sketching: Generating Drafts from Analogous Texts](https://mlsanity.com/paper/2106.07192/) (2021)
- [Mitigating Data Scarceness through Data Synthesis, Augmentation and Curriculum for Abstractive Summarization](https://mlsanity.com/paper/2109.08569/) (2021)
- [DomainSum: A Hierarchical Benchmark for Fine-Grained Domain Shift in Abstractive Text Summarization](https://mlsanity.com/paper/2410.15687/) (2024)
- [ReflectSumm: A Benchmark for Course Reflection Summarization](https://mlsanity.com/paper/2403.19012/) (2024)

## Abstract (arXiv)

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.