Pegasus Models See Docs: here Original TF 1 code here Authors: Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019 Maintained by: @sshleifer 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…
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