SimCLS
SimCLS is a framework for abstractive summarization presented in SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization. It is a two-stage approach consisting of a generator and a scorer. In the first stage, a large pre-trained model for abstractive summarization (the generator) is used to generate candidate summaries, whereas, in the second stage, the scorer assigns a score to each candidate given the source document. The final summary is the highest-scoring candidate.
This model is the scorer trained for summarization of XSum (paper, datasets). It should be used in conjunction with google/pegasus-xsum. See our Github repository for details on training, evaluation, and usage.
Usage
git clone https://github.com/andrejmiscic/simcls-pytorch.git
cd simcls-pytorch
pip3 install torch torchvision torchaudio transformers sentencepiece
from src.model import SimCLS, GeneratorType
summarizer = SimCLS(generator_type=GeneratorType.Pegasus,
generator_path="google/pegasus-xsum",
scorer_path="andrejmiscic/simcls-scorer-xsum")
article = "This is a news article."
summary = summarizer(article)
print(summary)
Results
All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap. See SimCLS paper for a description of baselines.
| System | Rouge-1 | Rouge-2 | Rouge-L |
|---|---|---|---|
| Pegasus | 47.21 | 24.56 | 39.25 |
| SimCLS paper | --- | --- | --- |
| Origin | 47.10 | 24.53 | 39.23 |
| Min | 40.97 | 19.18 | 33.68 |
| Max | 52.45 | 28.28 | 43.36 |
| Random | 46.72 | 23.64 | 38.55 |
| SimCLS | 47.61 | 24.57 | 39.44 |
| Our results | --- | --- | --- |
| Origin | 47.16, [46.85, 47.48] | 24.59, [24.25, 24.92] | 39.30, [38.96, 39.62] |
| Min | 41.06, [40.76, 41.34] | 18.30, [18.03, 18.56] | 32.70, [32.42, 32.97] |
| Max | 51.83, [51.53, 52.14] | 28.92, [28.57, 29.26] | 44.02, [43.69, 44.36] |
| Random | 46.47, [46.17, 46.78] | 23.45, [23.13, 23.77] | 38.28, [37.96, 38.60] |
| SimCLS | 47.17, [46.87, 47.46] | 23.90, [23.59, 24.23] | 38.96, [38.64, 39.29] |
Citation of the original work
@inproceedings{liu-liu-2021-simcls,
title = "{S}im{CLS}: A Simple Framework for Contrastive Learning of Abstractive Summarization",
author = "Liu, Yixin and
Liu, Pengfei",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-short.135",
doi = "10.18653/v1/2021.acl-short.135",
pages = "1065--1072",
}