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 CNN/DailyMail (paper, datasets). It should be used in conjunction with facebook/bart-large-cnn. 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.Bart,
generator_path="facebook/bart-large-cnn",
scorer_path="andrejmiscic/simcls-scorer-cnndm")
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 |
|---|---|---|---|
| BART | 44.16 | 21.28 | 40.90 |
| SimCLS paper | --- | --- | --- |
| Origin | 44.39 | 21.21 | 41.28 |
| Min | 33.17 | 11.67 | 30.77 |
| Max | 54.36 | 28.73 | 50.77 |
| Random | 43.98 | 20.06 | 40.94 |
| SimCLS | 46.67 | 22.15 | 43.54 |
| Our results | --- | --- | --- |
| Origin | 44.41, [44.18, 44.63] | 21.05, [20.80, 21.29] | 41.53, [41.30, 41.75] |
| Min | 33.43, [33.25, 33.62] | 10.97, [10.82, 11.12] | 30.57, [30.40, 30.74] |
| Max | 53.87, [53.67, 54.08] | 29.72, [29.47, 29.98] | 51.13, [50.92, 51.34] |
| Random | 43.94, [43.73, 44.16] | 20.09, [19.86, 20.31] | 41.06, [40.85, 41.27] |
| SimCLS | 46.53, [46.32, 46.75] | 22.14, [21.91, 22.37] | 43.56, [43.34, 43.78] |
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",
}