TLDR: Extreme Summarization of Scientific Documents
Isabel Alyssa Cachola, Kyle Shih-Huang Lo, Arman Cohan, Daniel S. Weld · 2020
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. Dataset constructionOverview We introduce SCITLDR, a new multitarget dataset of 5,411 TLDRs over 3,229 scientific papers in the computer science domain.3 The training set contains 1,992 papers, each with a single gold TLDR.The dev and test sets contain 619 and 618 papers each, with 1,452 and 1,967 TLDRs, respectively.This is unlike the majority of existing 3 See Appendix Table 9 for full venue breakdown.