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--- |
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language: en |
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tags: |
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- summarization |
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model-index: |
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- name: google/pegasus-cnn_dailymail |
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results: |
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- task: |
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type: summarization |
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name: Summarization |
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dataset: |
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name: cnn_dailymail |
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type: cnn_dailymail |
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config: 3.0.0 |
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split: test |
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metrics: |
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- name: ROUGE-1 |
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type: rouge |
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value: 43.1716 |
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verified: true |
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- name: ROUGE-2 |
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type: rouge |
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value: 20.7323 |
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verified: true |
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- name: ROUGE-L |
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type: rouge |
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value: 30.4769 |
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verified: true |
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- name: ROUGE-LSUM |
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type: rouge |
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value: 36.8168 |
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verified: true |
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- name: loss |
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type: loss |
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value: 2.030170202255249 |
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verified: true |
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- name: gen_len |
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type: gen_len |
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value: 77.8432 |
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verified: true |
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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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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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The following is copied from the authors' README. |
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# Mixed & Stochastic Checkpoints |
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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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| 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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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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(*) 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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The "Mixed & Stochastic" model has the following changes (from pegasus-large in the paper): |
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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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Citation |
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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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``` |
