SciTLDR

This repository contains the dataset, model weights, and generation code for our paper "TLDR: Extreme Summarization of Scientific Documents".

Demo

A running demo of our model can be found here.

Requirements

We use Fairseq to train and evaluate our models. To install all requirements, run pip install -r requirements.txt

For the evaluation, you will need files2rouge. Please install my fork of the repo.

Model Weights

bart.large.xsum.multitask-A

bart.large.xsum.multitask-AIC

Data Preprocessing

In order to format the data to work for the Fairseq library, run:

$ cd SciTLDR-Data
$ export TASK=SciTLDR-A # Choose from {A, AIC, FullText}
$ python to_stories.py $TASK # Convert to story format
$ chmod +x make_datafiles.sh
$ ./make_datafiles.sh # BPE preprocess

Evaluation

This code takes in a test.source file, in which each line is an input and outputs a test.hypo file with the predictions. It imports a test.jsonl file as a reference and stores the rouge score in test.hypo.score.

$ python evaluate.py SciTLDR-Data/SciTLDR-A /path/to/model/dir/ --checkpoint_file scitldr_ao_model.pt --beam 4 --lenpen 0.6

OR

$ python evaluate.py SciTLDR-Data/SciTLDR-AIC /path/to/model/dir/ --checkpoint_file scitldr_aic_model.pt --beam 2 --lenpen 0.2 

Citing

If you use our code, dataset, or model weights in your research, please cite "TLDR: Extreme Summarization of Scientific Documents."

@article{cachola2020tldr,
  title={{TLDR}: Extreme Summarization of Scientific Documents},
  author={Isabel Cachola and Kyle Lo and Arman Cohan and Daniel S. Weld},
  journal={arXiv:2004.15011},
  year={2020},
}

SciTLDR is an open-source project developed by the Allen Institute for Artificial Intelligence (AI2). AI2 is a non-profit institute with the mission to contribute to humanity through high-impact AI research and engineering.