Abstract
Scientific extreme summarization (TLDR) aims to form ultra-short summaries of scientific papers. Previous efforts on curating scientific TLDR datasets failed to scale up due to the heavy human annotation and domain expertise required. In this paper, we propose a simple yet effective approach to automatically extracting TLDR summaries for scientific papers from their citation texts. Based on the proposed approach, we create a new benchmark CiteSum without human annotation, which is around 30 times larger than the previous human-curated dataset SciTLDR. We conduct a comprehensive analysis of CiteSum, examining its data characteristics and establishing strong baselines. We further demonstrate the usefulness of CiteSum by adapting models pre-trained on CiteSum (named CITES) to new tasks and domains with limited supervision. For scientific extreme summarization, CITES outperforms most fully-supervised methods on SciTLDR without any fine-tuning and obtains state-of-the-art results with only 128 examples. For news extreme summarization, CITES achieves significant gains on XSum over its base model (not pre-trained on CiteSum), e.g., +7.2 ROUGE-1 zero-shot performance and state-of-the-art few-shot performance. For news headline generation, CITES performs the best among unsupervised and zero-shot methods on Gigaword.- Anthology ID:
- 2022.emnlp-main.750
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 10922–10935
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.750
- DOI:
- 10.18653/v1/2022.emnlp-main.750
- Cite (ACL):
- Yuning Mao, Ming Zhong, and Jiawei Han. 2022. CiteSum: Citation Text-guided Scientific Extreme Summarization and Domain Adaptation with Limited Supervision. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 10922–10935, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- CiteSum: Citation Text-guided Scientific Extreme Summarization and Domain Adaptation with Limited Supervision (Mao et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2022.emnlp-main.750.pdf