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