Abstract
Query-focused summarization has been considered as an important extension for text summarization. It aims to generate a concise highlight for a given query. Different from text summarization, query-focused summarization has long been plagued by the problem of lacking high-quality large-scale datasets. In this paper, we investigate the idea that whether we can integrate and transfer the knowledge of text summarization and question answering to assist the few-shot learning in query-focused summarization. Here, we propose prefix-merging, a prefix-based pretraining strategy for few-shot learning in query-focused summarization. Drawn inspiration from prefix-tuning, we are allowed to integrate the task knowledge from text summarization and question answering into a properly designed prefix and apply the merged prefix to query-focused summarization. With only a small amount of trainable parameters, prefix-merging outperforms fine-tuning on query-focused summarization. We further discuss the influence of different prefix designs and propose a visualized explanation for how prefix-merging works.- Anthology ID:
- 2022.emnlp-main.243
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3704–3714
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.243
- DOI:
- Cite (ACL):
- Ruifeng Yuan, Zili Wang, Ziqiang Cao, and Wenjie Li. 2022. Few-shot Query-Focused Summarization with Prefix-Merging. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 3704–3714, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Few-shot Query-Focused Summarization with Prefix-Merging (Yuan et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.emnlp-main.243.pdf