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--- |
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language: |
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- en |
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datasets: |
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- xsum |
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- scitldr |
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widget: |
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- 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." |
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license: "apache-2.0" |
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--- |
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# AI2 SciTLDR |
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Fairseq checkpoints from CATTS XSUM to Transformers BART (Abtract Only) |
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Original repository: [https://github.com/allenai/scitldr](https://github.com/allenai/scitldr) |
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## Demo |
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A running demo of AI2 model can be found [here](https://scitldr.apps.allenai.org). |
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### Citing |
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If you use code, dataset, or model weights in your research, please cite "TLDR: Extreme Summarization of Scientific Documents." |
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``` |
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@article{cachola2020tldr, |
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title={{TLDR}: Extreme Summarization of Scientific Documents}, |
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author={Isabel Cachola and Kyle Lo and Arman Cohan and Daniel S. Weld}, |
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journal={arXiv:2004.15011}, |
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year={2020}, |
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} |
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``` |
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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. |
