simcls

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",
}