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This is an implementation of the SeqCo (Sequence Level Contrastive Learning for Text Summarization) model described in Sequence Level Contrastive Learning for Text Summarization (In AAAI 2022)

pip install pyrouge==0.1.3
pip install pytorch-transformers==1.1.0

# For rouge-1.5.5.pl
sudo apt-get update
sudo apt-get install expat
sudo apt-get install libexpat-dev -y

# For files2fouge
git clone https://github.com/pltrdy/files2rouge.git
cd files2rouge
python setup_rouge.py
python setup.py install

1. Follow the instruction in https://github.com/abisee/cnn-dailymail to download and process into data-files such that test.source and test.target has one line for each non-tokenized sample.
2. sh seqco_scripts/bpe_preprocess.sh. In seqco_scripts/bpe_preprocess.sh, you need to change the --inputs to the path you store test.source and test.target
3. sh seqco_scripts/binarize.sh

sh seqco_scripts/train_cnndm.sh

sh seqco_scripts/infer_cnndm.sh

sh seqco_script/evaluate_sum.sh $PATH_TO_GENERATED_SUMMAY $CHECKPOINT

If you find our code is useful, please cite the following paper:

@article{xu2021sequence,
  title={Sequence Level Contrastive Learning for Text Summarization},
  author={Xu, Shusheng and Zhang, Xingxing and Wu, Yi and Wei, Furu},
  journal={arXiv preprint arXiv:2109.03481},
  year={2021}
}