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andrejmiscic/simcls-scorer-cnndm

sourceHugging Faceupdated 2y agoView on Hugging Face

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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 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

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.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

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",
}