TLDR: Extreme Summarization of Scientific Documents

  title={TLDR: Extreme Summarization of Scientific Documents},
  author={Isabel Cachola and Kyle Lo and Arman Cohan and Daniel S. Weld},
  • Isabel Cachola, Kyle Lo, +1 author Daniel S. Weld
  • Published in EMNLP 2020
  • Computer Science
  • We introduce TLDR generation for scientific papers, a new automatic summarization task with high source compression, requiring expert background knowledge and complex language understanding. To facilitate research on this task, we introduce SciTLDR, a dataset of 3.9K TLDRs. Furthermore, we introduce a novel annotation protocol for scalably curating additional gold summaries by rewriting peer review comments. We use this protocol to augment our test set, yielding multiple gold TLDRs for… CONTINUE READING
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