readme: ---
  language:
  - en
  datasets:
  - xsum
  - scitldr
  widget:
  - text: "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."
  license: "apache-2.0"
  
  ---
  
  # AI2 SciTLDR
  Fairseq checkpoints from CATTS XSUM to Transformers BART (Abtract Only)
  
  Original repository: [https://github.com/allenai/scitldr](https://github.com/allenai/scitldr)
  
  ## Demo
  A running demo of AI2 model can be found [here](https://scitldr.apps.allenai.org).
  
  ### Citing
  If you use code, dataset, or model weights in your research, please cite "TLDR: Extreme Summarization of Scientific Documents."
  
  ```
  @article{cachola2020tldr,
    title={{TLDR}: Extreme Summarization of Scientific Documents},
    author={Isabel Cachola and Kyle Lo and Arman Cohan and Daniel S. Weld},
    journal={arXiv:2004.15011},
    year={2020},
  }
  ```
  
  SciTLDR is an open-source project developed by the Allen Institute for Artificial Intelligence (AI2). AI2 is a non-profit institute with the mission to contribute to humanity through high-impact AI research and engineering.