Corresponding paper, accepted to ICML 2019: https://arxiv.org/abs/1810.05739.

Main requirements:

• python 3
• torch 0.4.0

Rest of python packages in requirements.txt. Tested in Docker, image = pytorch/pytorch:0.4_cuda9_cudnn7.

Execute inside scripts/:

bash setup_dirs.sh

bash install_python_pkgs.sh

python update_tensorboard.py

1. Download Yelp data: https://www.yelp.com/dataset and place files in datasets/yelp_dataset/
2. Run script to pre-process script and create train, val, test splits:

   bash scripts/preprocess_data.sh
   

3. Download subword tokenizer built on Yelp and place in datasets/yelp_dataset/processed/: link

1. Download summarization model and place in stable_checkpoints/sum/mlstm/yelp/batch_size_16-notes_cycloss_honly-sum_lr_0.0005-tau_2.0/: link
2. Download language model and place in stable_checkpoints/lm/mlstm/yelp/batch_size_512-lm_lr_0.001-notes_data260_fixed/: link
3. Download classification model and place in stable_checkpoints/clf/cnn/yelp/batch_size_256-notes_data260_fixed/: link

Download from: link. Each row contains "Input.business_id", "Input.original_review_<num>_id", "Input.original_review__<num>_", "Answer.summary", etc. The "Answer.summary" is the reference summary written by the Mechanical Turk worker.

Testing with pretrained mode. This will output and save the automated metrics. Results will be in outputs/eval/yelp/n_docs_8/unsup_<run_name>

NOTE: Unlike some conventions, 'gpus' option here represents the GPU ID (the one which is visible) and NOT the number of GPUs. Hence, for a machine with a single GPU, you will give gpus=0

python train_sum.py --mode=test --gpus=0 --batch_size=16 --notes=<run_name>

Training summarization model (using pre-trained language model and default hyperparams). The automated metrics results will be in checkpoints/sum/mlstm/yelp/<hparams>_<additional_notes>.:

python train_sum.py --batch_size=16 --gpus=0,1,2,3 --notes=<additional_notes>