This is the repositotry of GLIMPSE: Pragmatically Informative Multi-Document Summarization for Scholarly Reviews Paper | Code

Installation

• We use python 3.10 and CUDA 12.1

module load miniconda/3
module load cuda12

• First, create a virtual environment using:

conda create -n glimpse python=3.10

• Second, activate the environment and install pytorch:

conda activate glimpse 
conda install pytorch==2.1.1 pytorch-cuda=12.1 -c pytorch -c nvidia

• Finally, all remaining required packages could be installed with the requirements file:

pip install -r requirements

Data Loading

Step 1: Start by processing the input files from data.

python glimpse/data_loading/data_processing.py 

Generating Summaries and Computing RSA Scores

Step 2: Now, we generate candidate summaries and compute RSA scores for each candidate

• for extractive candidates, use the following command:

sbatch scripts/extractive.sh Path_of_Your_Processed_Dataset_Step1.csv

• for abstractive candidates, use either of the following commands:
  • In case the last batch is incomplete, you can add padding using --add-padding argument to complete it:

  sbatch scripts/abstractive.sh Path_of_Your_Processed_Dataset_Step1.csv --add-padding

  • If you want to remove the last incomplete batch, you can run the script without the argument:

  sbatch scripts/abstractive.sh Path_of_Your_Processed_Dataset_Step1.csv

rsasumm/ provides a python package with an implementation of RSA incremental decoding and RSA reranking of candidates. mds/ provides the experiment scripts and analysis for the MultiDocument Summarization task.

Citation

If you use this code, please cite the following papers:

      title={GLIMPSE: Pragmatically Informative Multi-Document Summarization for Scholarly Reviews}, 
      author={Maxime Darrin and Ines Arous and Pablo Piantanida and Jackie CK Cheung},
      year={2024},
      eprint={2406.07359},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2406.07359}, 
}