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SummN

Source code for ACL 2022 paper SUMM^N: A Multi-Stage Summarization Framework for Long InputDialogues and Documents

Update

  • Release some of the prediction files (*.hypo one sample each line) together with the checkpoints. Google Drive Link

Dependency

  • Install Fairseq according to their official instructions https://github.com/pytorch/fairseq
  • pip install -r requirements.txt to install the rest of the packages
  • We use python==3.7, pytorch==1.8.1 (cuda=11.1), and fairseq==0.10.0

Folder Structure

  • configure: the running configures for each dataset, such as number of stages, beam width etc.
  • dataset_loader: the python scripts to convert original dataset to the uniform format.
  • models: SummN model
    • data_segment: including source and target segmentation code;
    • gen_summary: inference on the source text and generate coarse summaries;
    • train_summarizor.sh: we use fairseq-train command to train the model.
  • scripts: all scripts to run experiments on different datasets.
  • utils: utilities such as config parser & dataset reader etc.
  • run.py the entrance of the code.

Training and Evaluation

Download the Datasets and Models

# bart cnn
wget https://dl.fbaipublicfiles.com/fairseq/models/bart.large.cnn.tar.gz
tar -xzvf bart.large.cnn.tar.gz

wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/encoder.json'
wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/vocab.bpe'
wget -N 'https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/dict.txt'

Training the Model

  • After we setup the datasets, setup the paths of scripts at scripts/{dataset name}.sh
  • Train the model by the command: bash scripts/{dataset name}.sh

Evaluation

  • First download the checkpoint from Google Drive
  • Then, setup the paths of scripts at scripts/{dataset name}.sh
  • Finally, specify the mode and checkpoint_dir in the running scripts. For instance,
python run.py --cfg ICSI.cfg \
 --dataset-path /data/yfz5488/fair/ICSI/ICSI_proprec \
 --output-path ./output/${RUN_NAME} \
 --save-intermediate \
 --cuda-devices 3 \
 --model-path $BART_PATH \
 --mode test \
 --checkpoint-dir path/to/checkpoints

And run this script to do the evaluation on test set only.

Add a New Task

It is easy to add new task/dataset into Summ-N.

  • First, add the configuration file in configure directory, one can write the cfg file following other files, e.g. configure/ICSI.cfg is a 3 stage config
  • Then write the dataset loader and add it to dataset_loader directory. dataset_loader/ICSI.py can be a good example
  • Finally, add the running parameters into scripts, following e.g. scripts/run_ICSI.sh
  • Run the training or evaluation by bash scripts/{Your Dataset}.sh

Citation

@inproceedings{zhang2021summn,
  title={Summ\^{} N: A Multi-Stage Summarization Framework for Long Input Dialogues and Documents},
  author={Zhang, Yusen and Ni, Ansong and Mao, Ziming and Wu, Chen Henry and Zhu, Chenguang and Deb, Budhaditya and Awadallah, Ahmed H and Radev, Dragomir and Zhang, Rui},
  booktitle={ACL 2022},
  year={2022}
}