Web: http://arxiv.org/abs/2205.06207

May 13, 2022, 1:11 a.m. | Yuning Mao, Ming Zhong, Jiawei Han

cs.CL updates on arXiv.org arxiv.org

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 …

arxiv domain adaptation summarization text

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