Cooperative Generator-Discriminator Networks

This repository contains the code used in the paper:

Discourse Understanding and Factual Consistency in Abstractive Summarization. Saadia Gabriel, Antoine Bosselut, Jeff Da, Ari Holtzman, Jan Buys, Kyle Lo, Asli Celikyilmaz and Yejin Choi. EACL 2021.

The repo contains

1. A cooperative generator-discriminator summarization framework for generating abstracts of scientific papers with various discourse and factuality objectives; and
2. A large-scale dataset of ArXiv scientific papers covering a broad range of domains.

Dataset (Currently contains all ArXiv examples for the intro -> abstract task)

Dataset Description

See here for a list of ArXiv paper categories

The dataset contains a json file for each paper intro/abstract pair with the category, article split into sentences, abstract split into sentences, and an id.

Instructions

Setting up data:

mkdir data

cd data

Download data from Google Drive

for train, test and val splits: tar -xvf {split}.tar

Training Generator:

Example w/ Bio summarization model: python train.py --data_dir ../data --dataset bio

Example w/ CS summarization model: python train.py --data_dir ../data --dataset cs

Decoding from Generator:

Example: python decode.py --load_epoch 12 --dataset bio --data_dir ../data --topk 3 --num_cands 30 --split test --model_dir ./model

Generations for each example are located in the file at ./generator/{dataset}_gpt2_gen/{split}/{split}-{id}.txt and the file contains a list of generated candidate summaries with every line of the file containing a different candidate summary

Discriminator Models:

The sequence prediction model (Cohan et al., 2019) for predicting abstract discourse role tags can be found with instructions and requirements here

The training script for the adjacency model is here

The token classification model for the factuality discriminator is here

Cooperative Reranking:

All the outputs from discriminator models should be combined into json files of the format {split}_gen_{candidate index}_{file_id}.json with the keys "labels" (from the discourse role model), "sentences" from the abstract, "tokens" and "scores" (from the token classification model). An example is given in the small_data folder.

Source data formatted for reranking can be found in the Google drive under "evaluation_formatted_data."

Example:

python disc_selection.py --subset aan --gen_folder ./gens --source_folder ./source_data --gen_model_path ./gen_model/model/best_params --aan_model_path ./aan_model/model/best_params --bio_model_path ./bio_model/model/best_params (rank based on various discriminators)

The output will be a json file containing scores for the original model ("prob"), the adjacency model ("adj"), the coverage model ("cov"), the order model ("order") and the fact model ("fact") for each candidate summary.

python assess.py --subset aan --gen_folder ./gens/ --source_folder ./gens (choose best for each discriminator)

---

@inproceedings{Gabriel2021DiscourseUA,
  title={Discourse Understanding and Factual Consistency in Abstractive Summarization},
  author={Saadia Gabriel and Antoine Bosselut and Jeff Da and Ari Holtzman and Jan Buys and Asli Çelikyilmaz and Yejin Choi},
  booktitle={EACL},
  year={2021}
}

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Code mainly adapted from HuggingFace Transformers (https://huggingface.co/transformers/), with additional acknowledgements given in the respective subdirectories.

Contact skgabrie@cs.washington.edu with questions or make a new issue.