CiteSum: Citation Text-guided Scientific Extreme Summarization and Domain Adaptation with Limited Supervision
Yuning Mao, Ming Zhong, Jiawei Han · 2022
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-theart 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. 1