Source code for the paper SummaReranker: A Multi-Task Mixture-of-Experts Re-ranking Framework for Abstractive Summarization.
Mathieu Ravaut, Shafiq Joty, Nancy F. Chen.
Accepted for publication at ACL 2022.
git clone https://github.com/Ravoxsg/SummaReranker.git
cd SummaReranker
conda create --name summa_reranker python=3.8.8
conda activate summa_reranker
pip install -r requirements.txt
We use HuggingFace datasets library to access and save each dataset. We save it as .txt file for the sources, and another one for the summaries, with 1 data point per line. For CNN/DM, we save one .txt file for every single data point.
For instance to download and save Reddit:
cd src/candidate_generation/
bash dataset.sh
Note that for Reddit TIFU, we make a custom 80/10/10 train/val/test split.
To match our results on Reddit TIFU, first double check that you have the following:
For training set, size is 33,704 and the first data point summary is:
got a toy train from first grade. used an old hot wheels ramp to fling it into the air and smash my ceiling fan globe.
For the validation set, size is 4,213 and the first data point summary is:
married a redditor. created a reddit account. lost many hours to reddit.
For the test set, size is 4,222 and the first data point summary is:
laughed at baby boner...it turned into a super soaker.
If you just want a demo (in a single file) of SummaReranker on a single data point (default: CNN/DM), run:
cd src/summareranker/
CUDA_VISIBLE_DEVICES=0 python demo.py
SummaReranker takes as input a set of summary candidates from a given sequence-to-sequence model (PEGASUS, BART) and a given decoding method (beam search, diverse beam search, top-p sampling, top-k sampling).
For instance with PEGASUS on Reddit validation set, and with diverse beam search:
CUDA_VISIBLE_DEVICES=0 bash candidate_generation.sh
Generating summary candidates should take a few hours on the validation or test sets of CNN/DM, XSum or Reddit.
Note that for Reddit, you need to fine-tune the model on your training split prior to generating candidates.
To evaluate SummaReranker, we need to score each summary candidate with all the metrics of interest (ROUGE-1/2/L, BERTScore, BARTScore, etc).
For instance to score PEGASUS diverse beam search candidates on Reddit validation set with ROUGE-1:
CUDA_VISIBLE_DEVICES=0 bash scores.sh
Scoring all candidates should take a few minutes with ROUGE metrics on the validation or test sets of CNN/DM, XSum or Reddit.
CNN/DM checkpoint (trained on beam search + diverse beam search candidates, for ROUGE-1/2/L metrics): here
XSum checkpoint (trained on beam search + diverse beam search candidates, for ROUGE-1/2/L metrics): here
Reddit checkpoint (trained on beam search + diverse beam search candidates, for ROUGE-1/2/L metrics): here
For instance, to run SummaReranker trained for ROUGE-1/2/L on PEGASUS (beam search + diverse beam search candidates) on Reddit validation set:
cd ../summareranker/
CUDA_VISIBLE_DEVICES=0 bash evaluate.sh
Make sure that the argument --load_model_path points to where you placed the SummaReranker checkpoint.
For training, SummaReranker follows a cross-validation approach: the training set is split in two, and we train one model on each half, to then infer it and use its predictions on the other half. We also need a third model trained on the entire training set (for the transfer setup at inference time), which we re-train ourselves for Reddit.
For instance with PEGASUS on Reddit:
cd ../base_model_finetuning/
CUDA_VISIBLE_DEVICES=0 bash train_base_models.sh
Note that this single script performs all the tasks of splitting the training set, then training models and on each half, and training a model on the entire set.
For models trained on the entire training set of CNN/DM and XSum, I used the public HuggingFace checkpoints:
CNN/DM: https://huggingface.co/google/pegasus-cnn_dailymail
XSum: https://huggingface.co/google/pegasus-xsum
Here's a link to download the PEGASUS-large fine-tuned on 100% of my training set of Reddit: here
Then, we need to get summary candidates on the training, validation and test sets.
For instance with PEGASUS on Reddit with diverse beam search:
cd ../candidate_generation/
CUDA_VISIBLE_DEVICES=0 bash candidate_generation_train.sh
Generating summary candidates on the entire datasets should take up to a few days.
Next, we need to score the summary candidates on the training, validation and test sets for each of the metrics.
For instance to score PEGASUS diverse beam search candidates on Reddit with ROUGE-1/2/L:
CUDA_VISIBLE_DEVICES=0 bash scores_train.sh
Scoring all candidates should take a few minutes with ROUGE metrics.
For instance, to train SummaReranker trained for ROUGE-1/2/L on PEGASUS diverse beam search candidates on Reddit:
cd ../summareranker/
CUDA_VISIBLE_DEVICES=0 bash train.sh
If you find our paper or this project helps your research, please kindly consider citing our paper in your publication.
@article{ravaut2022summareranker,
title={SummaReranker: A Multi-Task Mixture-of-Experts Re-ranking Framework for Abstractive Summarization},
author={Ravaut, Mathieu and Joty, Shafiq and Chen, Nancy F},
journal={arXiv preprint arXiv:2203.06569},
year={2022}
}
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