Data Augmentation for Abstractive Query-Focused Multi-Document Summarization (AAAI 2021)

This is the implementation of the paper Data Augmentation for Abstractive Query-Focused Multi-Document Summarization.

Prerequisites

• Python 3.6+

• [PyTorch 1.0] (http://pytorch.org/)

• Install all the required packages from requirements.txt file.

  pip install -r requirements.txt
  

• Download the processed datasets (in pytorch format) and setup some folders and repos by running the following command:

  python setup.py
  

  if you face any issues in downloading the datasets with the above code (setup.py), directly download the datasets from here: wikisum, wikisum-query, qmds-cnn, qmds-cnn-query. Run the above code with the following argument to setup everything else except the datasets.

  python setup.py --ignore_datasets
  

• If you face any issues with running ROUGE evaluation, checkout this link.

• Some codes are borrowed from: hiersumm and ONMT.

Usage

To train the model:

DATASET=[CNNDM/WIKI] MODEL_TYPE=[hier/he/order/query/heq/heo/hero] bash run_experiments.sh 

To test the model:

DATASET=[CNNDM/WIKI] MODEL_TYPE=[hier/he/order/query/heq/heo/hero] bash test.sh 

Few points to note:

• Various model types (MODEL_TYPE):
  • hier: Baseline model (Hierarchical Transformers)
  • he: HS w/ Hierarchical Encodings
  • order: HS w/ Ordering Component
  • query: HS w/ Query Encoding
  • heq: HS-Joint Model (Hierachical Encodings + Query Encoding)
  • heo: HS-Joint Model (Hierachical Encodings + Ordering Component)
  • hero: HS-Joint Model (all three components combined)
• We tested our models on Nvidia P-100s 16GB. Each experiments uses 4 GPUs. If you have fewer gpus or memory, set BATCH_SIZE, VISIBLE_GPUS, ACCUM_COUNT accordingly.
• data, vocab, and model paths are set to default locations. Set these variables if you want to use different paths.

Reference

If you find this code helpful, please consider citing the following paper:

@inproceedings{pasunuru2021data,
    title={Data Augmentation for Abstractive Query-Focused Multi-Document Summarization},
    author={Pasunuru, Ramakanth and Celikyilmaz, Asli and Galley, Michel and Xiong, Chenyan and Zhang, Yizhe and Bansal, Mohit and Gao, Jianfeng},
    booktitle={AAAI},
    year={2021}
}