# A Pilot Study of Domain Adaptation Effect for Neural Abstractive Summarization

arXiv: https://arxiv.org/abs/1707.07062 · HTML: https://mlsanity.com/paper/1707.07062/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2017.

## Built on by (most cited first, 12 of 12)

- [AdaptSum: Towards Low-Resource Domain Adaptation for Abstractive Summarization](https://mlsanity.com/paper/2103.11332/) (2021)
- [ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining](https://mlsanity.com/paper/2106.00829/) (2021)
- [Low-Resource Dialogue Summarization with Domain-Agnostic Multi-Source Pretraining](https://mlsanity.com/paper/2109.04080/) (2021)
- [Improve Query Focused Abstractive Summarization by Incorporating Answer Relevance](https://mlsanity.com/paper/2105.12969/) (2021)
- [Domain-Oriented Prefix-Tuning: Towards Efficient and Generalizable Fine-tuning for Zero-Shot Dialogue Summarization](https://mlsanity.com/paper/2204.04362/) (2022)
- [Self-Repetition in Abstractive Neural Summarizers](https://mlsanity.com/paper/2210.08145/) (2022)
- [Flexible and Adaptable Summarization via Expertise Separation](https://mlsanity.com/paper/2406.05360/) (2024)
- [DomainSum: A Hierarchical Benchmark for Fine-Grained Domain Shift in Abstractive Text Summarization](https://mlsanity.com/paper/2410.15687/) (2024)
- [PROM: A Phrase-level Copying Mechanism with Pre-training for Abstractive Summarization](https://mlsanity.com/paper/2305.06647/) (2023)
- [Generative Long-form Question Answering: Relevance, Faithfulness and Succinctness](https://mlsanity.com/paper/2211.08386/) (2022)
- [Summarization from Leaderboards to Practice: Choosing A Representation Backbone and Ensuring Robustness](https://mlsanity.com/paper/2306.10555/) (2023)
- [Guiding ChatGPT to Generate Salient Domain Summaries](https://mlsanity.com/paper/2406.01070/) (2024)

## Abstract (arXiv)

We study the problem of domain adaptation for neural abstractive summarization. We make initial efforts in investigating what information can be transferred to a new domain. Experimental results on news stories and opinion articles indicate that neural summarization model benefits from pre-training based on extractive summaries. We also find that the combination of in-domain and out-of-domain setup yields better summaries when in-domain data is insufficient. Further analysis shows that, the model is capable to select salient content even trained on out-of-domain data, but requires in-domain data to capture the style for a target domain.