# Exploring Domain Shift in Extractive Text Summarization

arXiv: https://arxiv.org/abs/1908.11664 · HTML: https://mlsanity.com/paper/1908.11664/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2019.

## Built on by (most cited first, 15 of 15)

- [QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization](https://mlsanity.com/paper/2104.05938/) (2021)
- [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)
- [Automatic Text Summarization Methods: A Comprehensive Review](https://mlsanity.com/paper/2204.01849/) (2022)
- [Domain-Oriented Prefix-Tuning: Towards Efficient and Generalizable Fine-tuning for Zero-Shot Dialogue Summarization](https://mlsanity.com/paper/2204.04362/) (2022)
- [Demoting the Lead Bias in News Summarization via Alternating Adversarial Learning](https://mlsanity.com/paper/2105.14241/) (2021)
- [Flexible and Adaptable Summarization via Expertise Separation](https://mlsanity.com/paper/2406.05360/) (2024)
- [Unsupervised Multi-Granularity Summarization](https://mlsanity.com/paper/2201.12502/) (2022)
- [Understanding Position Bias Effects on Fairness in Social Multi-Document Summarization](https://mlsanity.com/paper/2405.01790/) (2024)
- [DomainSum: A Hierarchical Benchmark for Fine-Grained Domain Shift in Abstractive Text Summarization](https://mlsanity.com/paper/2410.15687/) (2024)
- [BioCopy: A Plug-And-Play Span Copy Mechanism in Seq2Seq Models](https://mlsanity.com/paper/2109.12533/) (2021)
- [PROM: A Phrase-level Copying Mechanism with Pre-training for Abstractive Summarization](https://mlsanity.com/paper/2305.06647/) (2023)
- [Guiding ChatGPT to Generate Salient Domain Summaries](https://mlsanity.com/paper/2406.01070/) (2024)
- [Enhancing Health Mention Classification Performance: A Study on Advancements in Parameter Efficient Tuning](https://mlsanity.com/paper/2504.21685/) (2025)

## Abstract (arXiv)

Although domain shift has been well explored in many NLP applications, it still has received little attention in the domain of extractive text summarization. As a result, the model is under-utilizing the nature of the training data due to ignoring the difference in the distribution of training sets and shows poor generalization on the unseen domain. With the above limitation in mind, in this paper, we first extend the conventional definition of the domain from categories into data sources for the text summarization task. Then we re-purpose a multi-domain summarization dataset and verify how the gap between different domains influences the performance of neural summarization models. Furthermore, we investigate four learning strategies and examine their abilities to deal with the domain shift problem. Experimental results on three different settings show their different characteristics in our new testbed. Our source code including textitBERT-based, textitmeta-learning methods for multi-domain summarization learning and the re-purposed dataset textscMulti-SUM will be available on our project: urlhttp://pfliu.com/TransferSum/.