Hercules: Attributable and Scalable Opinion Summarization

Code for the paper "Attributable and Scalable Opinion Summarization", Tom Hosking, Hao Tang & Mirella Lapata (ACL 2023).

By representing sentences from reviews as paths through a discrete hierarchy, we can generate abstractive summaries that are informative, attributable and scale to hundreds of input reviews.

Setup

Create a fresh environment:

conda create -n herculesenv python=3.9
conda activate herculesenv

or

python3 -m venv herculesenv
source herculesenv/bin/activate

Then install dependencies:

pip install -r requirements.txt

Download data/models:

• Space -> ./data/opagg/
• AmaSum -> ./data/opagg/
• Trained checkpoints -> ./models

Tested with Python 3.9.

Evaluation with trained models

See ./examples/Space-Eval.ipynb

or

from torchseq.utils.model_loader import model_from_path
from torchseq.metric_hooks.hrq_agg import HRQAggregationMetricHook

model_slug = 'hercules_space' # Which model to load?

instance = model_from_path('./models/' + model_slug, output_path='./runs/', data_path='./data/', silent=True)

scores, res = HRQAggregationMetricHook.eval_generate_summaries_and_score(instance.config, instance, test=True)

print("Model {:}: Abstractive R2 = {:0.2f}, Extractive R2 = {:0.2f}".format(model_slug, scores['abstractive']['rouge2'], scores['extractive']['rouge2']))

Training on SPACE/AmaSum from scratch

To train on SPACE, download the datasets (as above) then you should just be able to run:

torchseq --train --reload_after_train --validate --config ./configs/hercules_space.json

Training on a new dataset (WIP)

You will need to:

• Install allennlp==2.10.1 and allennlp-models==2.10.1 via pip (ignore the warnings about version conflicts)
• Make a copy of your dataset in a format expected by the script below
• Run the dataset filtering scripts ./scripts/opagg_filter_space.py and ./scripts/opagg_filter_space_eval.py
• Run the script to generate training pairs ./scripts/generate_opagg_pairs.py
• Make a copy of one of the training configs and update to point at your data
• Finally, train the model!

torchseq --train --reload_after_train --validate --config ./configs/{YOUR_CONFIG}.json

Please feel free to raise a Github issue or email me if you run into any difficulties!

Citation

@inproceedings{hosking-etal-2023-attributable,
  title = "Attributable and Scalable Opinion Summarization",
  author = "Hosking, Tom  and
      Tang, Hao  and
      Lapata, Mirella",
  booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
  month = jul,
  year = "2023",
  address = "Toronto, Canada",
  publisher = "Association for Computational Linguistics",
  url = "https://aclanthology.org/2023.acl-long.473",
  pages = "8488--8505",
}