Skip to content

Latest commit

 

History

History
51 lines (41 loc) · 1.68 KB

README.md

File metadata and controls

51 lines (41 loc) · 1.68 KB
Raw

Global-Encoding

This is the code for our paper Global Encoding for Abstractive Summarization, https://arxiv.org/abs/1805.03989

Requirements

  • Ubuntu 16.0.4
  • Python 3.5
  • Pytorch 0.4.1 (updated)
  • pyrouge

In order to use pyrouge, set rouge path with the line below:

pyrouge_set_rouge_path RELEASE-1.5.5/

It seems that some user have met problems with pyrouge, so I have updated the script, and users can put the directory "RELEASE-1.5.5" in your home directory and set rouge path to it (or run the command "chmod 777 RELEASE-1.5.5" for the permission).

Preprocessing

python3 preprocess.py -load_data path_to_data -save_data path_to_store_data

Remember to put the data into a folder and name them train.src, train.tgt, valid.src, valid.tgt, test.src and test.tgt, and make a new folder inside called data

Training

python3 train.py -log log_name -config config_yaml -gpus id

Create your own yaml file for hyperparameter setting.

Evaluation

python3 train.py -log log_name -config config_yaml -gpus id -restore checkpoint -mode eval

Citation

If you use this code for your research, please kindly cite our paper:

@inproceedings{globalencoding,
  title     = {Global Encoding for Abstractive Summarization},
  author    = {Junyang Lin and Xu Sun and Shuming Ma and Qi Su},
  booktitle = {{ACL} 2018},
  year      = {2018}
}