TLDR: Extreme Summarization of Scientific Documents

• URL: http://arxiv.org/abs/2004.15011v3
• Date: Thu, 8 Oct 2020 22:41:44 GMT
• Title: TLDR: Extreme Summarization of Scientific Documents
• Authors: Isabel Cachola, Kyle Lo, Arman Cohan, Daniel S. Weld
• Abstract summary: SciTLDR is a dataset of 5.4K TLDRs over 3.2K papers. We propose CATTS, a simple yet effective learning strategy for generating TLDRs. Data and code are publicly available at https://www.allenai.com/scitldr.
• Score: 38.11051158313414
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce TLDR generation, a new form of extreme summarization, for scientific papers. TLDR generation involves high source compression and requires expert background knowledge and understanding of complex domain-specific language. To facilitate study on this task, we introduce SciTLDR, a new multi-target dataset of 5.4K TLDRs over 3.2K papers. SciTLDR contains both author-written and expert-derived TLDRs, where the latter are collected using a novel annotation protocol that produces high-quality summaries while minimizing annotation burden. We propose CATTS, a simple yet effective learning strategy for generating TLDRs that exploits titles as an auxiliary training signal. CATTS improves upon strong baselines under both automated metrics and human evaluations. Data and code are publicly available at https://github.com/allenai/scitldr.

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