Source code for ACL 2022 paper DYLE: Dynamic Latent Extraction for Abstractive Long-Input Summarization

Install dependencies via:

conda create -n dyle python=3.9.6
conda activate dyle
conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=11.1 -c pytorch -c conda-forge
pip install nltk==3.6.2 pyrouge==0.1.3 transformers==4.8.1 rouge==1.0.0 datasets==1.11.0

• dataloaders: the python scripts to convert original dataset to the uniform format.
• oracle: Scripts to generate extractive oracles
• utils: Various utility functions, such as cleaning and rouge
• Experiment.py: Main file for our model
• config.py: Set model configuration
• Modules: Contains implementation of our dynamic extraction module
• test.py: Run test set
• train.py: Train the model

• Download QMSum dataset from https://github.com/Yale-LILY/QMSum
• Download GovReport dataset from https://github.com/luyang-huang96/LongDocSum/tree/main/Model
• Download ArXiv from Hugging Face at https://huggingface.co/datasets/scientific_papers
• We provide cleaned dataset with oracle (in jsonl) for QMSum and GovReport from Google Drive. Download them and place them under data/
• ArXiv dataset and oracles are loaded separately

• After we clean the datasets and process the oracles, setup the paths of scripts at dataloaders/*.py
• Set the self.target_task flag in config.py to choose the target task
• Train the model by the command:

python train.py

• The code reproduced using the PyTorch Lightning library can be found at this link

• First download the checkpoint from Google Drive and place the folders under ./outputs/saved_model/
• Set the self.target_task flag in config.py to choose the target task

python test.py

@inproceedings{mao2021dyle,
 title={DYLE: Dynamic Latent Extraction for Abstractive Long-Input Summarization},
 author={Mao, Ziming and Wu, Chen Henry and Ni, Ansong and Zhang, Yusen and Zhang, Rui and Yu, Tao and Deb, Budhaditya and Zhu, Chenguang and Awadallah, Ahmed H and Radev, Dragomir},
 booktitle={ACL 2022},
 year={2022}
}