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+ ---
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+ language: en
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+ datasets:
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+ - xsum
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+ tags:
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+ - summarization
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+ ---
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+
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+ ### Pegasus Models
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+ See Docs: [here](https://huggingface.co/transformers/master/model_doc/pegasus.html)
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+ Original TF 1 code [here](https://github.com/google-research/pegasus)
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+
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+ Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019
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+ Maintained by: [@sshleifer](https://twitter.com/sam_shleifer)
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+ Task: Summarization
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+
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+ The following is copied from the authors' README.
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+
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+ # Mixed & Stochastic Checkpoints
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+
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+ We train a pegasus model with sampled gap sentence ratios on both C4 and HugeNews, and stochastically sample important sentences. The updated the results are reported in this table.
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+
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+ | dataset | C4 | HugeNews | Mixed & Stochastic|
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+ | ---- | ---- | ---- | ----|
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+ | xsum | 45.20/22.06/36.99 | 47.21/24.56/39.25 | 47.60/24.83/39.64|
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+ | cnn_dailymail | 43.90/21.20/40.76 | 44.17/21.47/41.11 | 44.16/21.56/41.30|
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+ | newsroom | 45.07/33.39/41.28 | 45.15/33.51/41.33 | 45.98/34.20/42.18|
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+ | multi_news | 46.74/17.95/24.26 | 47.52/18.72/24.91 | 47.65/18.75/24.95|
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+ | gigaword | 38.75/19.96/36.14 | 39.12/19.86/36.24 | 39.65/20.47/36.76|
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+ | wikihow | 43.07/19.70/34.79 | 41.35/18.51/33.42 | 46.39/22.12/38.41 *|
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+ | reddit_tifu | 26.54/8.94/21.64 | 26.63/9.01/21.60 | 27.99/9.81/22.94|
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+ | big_patent | 53.63/33.16/42.25 | 53.41/32.89/42.07 | 52.29/33.08/41.66 *|
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+ | arxiv | 44.70/17.27/25.80 | 44.67/17.18/25.73 | 44.21/16.95/25.67|
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+ | pubmed | 45.49/19.90/27.69 | 45.09/19.56/27.42 | 45.97/20.15/28.25|
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+ | aeslc | 37.69/21.85/36.84 | 37.40/21.22/36.45 | 37.68/21.25/36.51|
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+ | billsum | 57.20/39.56/45.80 | 57.31/40.19/45.82 | 59.67/41.58/47.59|
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+
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+ The "Mixed & Stochastic" model has the following changes:
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+ - trained on both C4 and HugeNews (dataset mixture is weighted by their number of examples).
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+ - trained for 1.5M instead of 500k (we observe slower convergence on pretraining perplexity).
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+ - the model uniformly sample a gap sentence ratio between 15% and 45%.
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+ - importance sentences are sampled using a 20% uniform noise to importance scores.
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+ - the sentencepiece tokenizer is updated to be able to encode newline character.
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+
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+
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+ (*) the numbers of wikihow and big_patent datasets are not comparable because of change in tokenization and data:
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+ - wikihow dataset contains newline characters which is useful for paragraph segmentation, the C4 and HugeNews model's sentencepiece tokenizer doesn't encode newline and loose this information.
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+ - we update the BigPatent dataset to preserve casing, some format cleanings are also changed, please refer to change in TFDS.
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+
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+
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+ The "Mixed & Stochastic" model has the following changes (from pegasus-large in the paper):
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+
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+
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+ trained on both C4 and HugeNews (dataset mixture is weighted by their number of examples).
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+ trained for 1.5M instead of 500k (we observe slower convergence on pretraining perplexity).
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+ the model uniformly sample a gap sentence ratio between 15% and 45%.
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+ importance sentences are sampled using a 20% uniform noise to importance scores.
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+ the sentencepiece tokenizer is updated to be able to encode newline character.
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+
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+
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+ Citation
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+ ```
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+
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+
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+ @misc{zhang2019pegasus,
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+ title={PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization},
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+ author={Jingqing Zhang and Yao Zhao and Mohammad Saleh and Peter J. Liu},
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+ year={2019},
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+ eprint={1912.08777},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CL}
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+ }
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+ ```