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andrejmiscic/
simcls-scorer-xsum

Name: simcls-scorer-xsum
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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

bash

git clone https://github.com/andrejmiscic/simcls-pytorch.git
cd simcls-pytorch
pip3 install torch torchvision torchaudio transformers sentencepiece

python

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

bibtex

@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",
}