Pytorch implementation of "Get To The Point: Summarization with Pointer-Generator Networks"

About this repository

This repo contains an Pytorch implementation for the ACL 2017 paper Get To The Point: Summarization with Pointer-Generator Networks. The code framework is based on TextBox.

Environment

• python >= 3.8.11
• torch >= 1.6.0

Run install.sh to install other requirements.

Dataset

The processed dataset can be downloaded from Google Drive. Once finished, unzip the datafiles (train.src, train.tgt, ...) to ./data.

An overview of dataset: train: 287113 cases, dev: 13368 cases, test: 11490 cases

Paramters

# overall settings
data_path: 'data/'
checkpoint_dir: 'saved/'
generated_text_dir: 'generated/'
# dataset settings
max_vocab_size: 50000
src_len: 400
tgt_len: 100

# model settngs
decoding_strategy: 'beam_search'
beam_size: 4
is_attention: True
is_pgen: True
is_coverage: True
cov_loss_lambda: 1.0

Log file is located in ./log, more details can be found in yamls.

Note: Distributed Data Parallel (DDP) is not supported yet.

Train & Evaluation

From scratch run fire.py.

if __name__ == '__main__':
    config = Config(config_dict={'test_only': False,
                                 'load_experiment': None})
    train(config)

If you want to resume from a checkpoint, just set the 'load_experiment': './saved/$model_name$.pth'. Similarly, when 'test_only' is set to True, 'load_experiment' is required.

Results

The best model is trained on a TITAN Xp GPU (8GB usage).

Training loss

Ablation study

Note: The architecture of the Seq2Seq model is based on lstm, I hope I can replace it with transformer in the future.