Pytorch implementation of the EMNLP-2021 paper: Low-Resource Dialogue Summarization with Domain-Agnostic Multi-Source Pretraining.

• Python 3.7.10

• pytorch 1.7.0+cu11.0

• py-rouge 1.1

• transformers 4.0.0

• multiprocess 0.70.11.1

• tensorboardX 2.1

• torchtext 0.4.0

• nltk 3.6.2

• RTX 3090 GPU

• CUDA 11.1

All the datastes used in our work are available at Google Drive or Baidu Pan (extract code: wwsd), including the multi-source pretraining data and the dialogue summary data.

• Download BERT checkpoints here and put BERT checkpoints into the directory bert like this:

   --- bert
     |
     |--- bert_base_uncased
        |
        |--- config.json
        |
        |--- pytorch_model.bin
        |
        |--- vocab.txt
  

• Download json files from the above data links and put them into the directory json_data like this:

   --- json_data
     |
     |--- samsum
     |
     |--- adsc
     |
     ...
  

• Pre-process dialogue summary datasets (e.g., the SAMSum training data).

  PYTHONPATH=. python ./src/preprocess.py -type train -raw_path json_data/samsum -save_path torch_data/samsum -log_file logs/json_to_data_samsum.log -truncated -n_cpus 4
  

• Pre-process multi-source pretraining datasets and mix them up.

  PYTHONPATH=. python ./src/preprocess.py -raw_path json_data -save_path torch_data/all -log_file logs/json_to_data.log -truncated -n_cpus 40 -mix_up
  

• Pretrain DAMS on the multi-source datasets.

  PYTHONPATH=. python ./src/main.py -mode train -data_path torch_data/all/data -model_path models/pretrain -log_file logs/pretrain.log -sep_optim -pretrain -visible_gpus 0,1 -pretrain_steps 250000 -port 10000
  

• Fine-tune DAMS on the SAMSum training set.

  PYTHONPATH=. python ./src/main.py -mode train -data_path torch_data/samsum/samsum -model_path models/samsum -log_file logs/samsum.train.log -visible_gpus 0 -warmup_steps 1000 -lr 0.001 -train_from models/pretrain/model_step_250000.pt -train_from_ignore_optim -train_steps 50000
  

• Validate DAMS on the SAMSum validation set.

  PYTHONPATH=. python ./src/main.py -mode validate -data_path torch_data/samsum/samsum -log_file logs/samsum.val.log -val_all -alpha 0.95 -model_path models/samsum -result_path results/samsum/samsum -visible_gpus 0 -min_length 15 -beam_size 3 -test_batch_ex_size 50
  

• Test DAMS.

  Zero-shot test on the SAMSum test set using the pretrained model.

  PYTHONPATH=. python ./src/main.py -mode test -data_path torch_data/samsum/samsum -log_file logs/samsum.test.log -alpha 0.95 -test_from models/pretrain/model_step_250000.pt -result_path results/samsum/samsum -visible_gpus 0 -min_length 15 -beam_size 3 -test_batch_ex_size 50
  

  Regular test on the SAMSum test set using the best validated model.

  PYTHONPATH=. python ./src/main.py -mode test -data_path torch_data/samsum/samsum -log_file logs/samsum.test.log -alpha 0.95 -test_from models/samsum/model_step_xxx.pt -result_path results/samsum/samsum -visible_gpus 0 -min_length 15 -beam_size 3 -test_batch_ex_size 50
  

  Transfer to the ADSC test set.

  PYTHONPATH=. python ./src/main.py -mode test -data_path torch_data/adsc/adsc -log_file logs/adsc.test.log -alpha 0.95 -test_from models/samsum/model_step_xxx.pt -result_path results/adsc/adsc -visible_gpus 0 -min_length 100 -beam_size 3 -test_batch_ex_size 50
  

@inproceedings{
	zou-etal-2021-low,
	title = "Low-Resource Dialogue Summarization with Domain-Agnostic Multi-Source Pretraining",
	author = "Zou, Yicheng  and Zhu, Bolin  and Hu, Xingwu  and Gui, Tao  and Zhang, Qi",
	booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
	month = nov,
	year = "2021",
	address = "Online and Punta Cana, Dominican Republic",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/2021.emnlp-main.7",
	pages = "80--91"
}