Edisum

This repository contains the PyTorch implementation for the models and experiments in the paper Edisum: Summarizing and Explaining Wikipedia Edits at Scale

@article{šakota2024edisum,
      title={Edisum: Summarizing and Explaining Wikipedia Edits at Scale}, 
      author={Marija Šakota and Isaac Johnson and Guosheng Feng and Robert West},
      journal={arXiv preprint arXiv:2404.03428}
      year={2024}
}

Please consider citing our work, if you found the provided resources useful.

1. Setup

Start by cloning the repository:

git clone https://github.com/epfl-dlab/edisum.git

We recommend creating a new conda virtual environment as follows:

conda env create -f environment.yml

This command also installs all the necessary packages.

2. Downloading data and models

The data is available on huggingface and can be loaded with:

from datasets import load_dataset
dataset = load_dataset("msakota/edisum_dataset")

Alternatively, to download the collected data for the experiments, run:

bash ./download_data.sh

For downloading the trained models (available on huggingface), run:

bash ./download_models.sh

3. Usage

Training

To train a model from scratch on the desired data, run:

DATA_DIR="./data/100_perc_synth_data/" # specify a directory where training data is located
RUN_NAME="train_longt5_100_synth"
python run_train.py run_name=$RUN_NAME dir=$DATA_DIR +experiment=finetune_longt5

Inference

To run inference on a trained model:

DATA_DIR="./data/100_perc_synth_data/" # specify a directory where training data is located
CHECKPOINT_PATH="./models/edisum_100.ckpt" # specify path to the trained model
RUN_NAME="inference_longt5_100_synth"
python run_inference.py run_name=$RUN_NAME dir=$DATA_DIR checkpoint_path=$CHECKPOINT_PATH +experiment=inference_longt5

License

This project is licensed under the terms of the MIT license.