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--- |
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language: |
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- en |
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tags: |
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- simcls |
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datasets: |
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- billsum |
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--- |
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# SimCLS |
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SimCLS is a framework for abstractive summarization presented in [SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization](https://arxiv.org/abs/2106.01890). |
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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. |
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This model is the *scorer* trained for summarization of BillSum ([paper](https://arxiv.org/abs/1910.00523), [datasets](https://huggingface.co/datasets/billsum)). It should be used in conjunction with [google/pegasus-billsum](https://huggingface.co/google/pegasus-billsum). See [our Github repository](https://github.com/andrejmiscic/simcls-pytorch) for details on training, evaluation, and usage. |
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## Usage |
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```bash |
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git clone https://github.com/andrejmiscic/simcls-pytorch.git |
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cd simcls-pytorch |
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pip3 install torch torchvision torchaudio transformers sentencepiece |
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``` |
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```python |
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from src.model import SimCLS, GeneratorType |
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summarizer = SimCLS(generator_type=GeneratorType.Pegasus, |
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generator_path="google/pegasus-billsum", |
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scorer_path="andrejmiscic/simcls-scorer-billsum") |
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document = "This is a legal document." |
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summary = summarizer(document) |
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print(summary) |
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``` |
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### Results |
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All of our results are reported together with 95% confidence intervals computed using 10000 iterations of bootstrap. See [SimCLS paper](https://arxiv.org/abs/2106.01890) for a description of baselines. |
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We believe the discrepancies of Rouge-L scores between the original Pegasus work and our evaluation are due to the computation of the metric. Namely, we use a summary level Rouge-L score. |
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| System | Rouge-1 | Rouge-2 | Rouge-L\* | |
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|-----------------|----------------------:|----------------------:|----------------------:| |
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| Pegasus | 57.31 | 40.19 | 45.82 | |
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| **Our results** | --- | --- | --- | |
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| Origin | 56.24, [55.74, 56.74] | 37.46, [36.89, 38.03] | 50.71, [50.19, 51.22] | |
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| Min | 44.37, [43.85, 44.89] | 25.75, [25.30, 26.22] | 38.68, [38.18, 39.16] | |
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| Max | 62.88, [62.42, 63.33] | 43.96, [43.39, 44.54] | 57.50, [57.01, 58.00] | |
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| Random | 54.93, [54.43, 55.43] | 35.42, [34.85, 35.97] | 49.19, [48.68, 49.70] | |
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| **SimCLS** | 57.49, [57.01, 58.00] | 38.54, [37.98, 39.10] | 51.91, [51.39, 52.43] | |
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### Citation of the original work |
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```bibtex |
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@inproceedings{liu-liu-2021-simcls, |
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title = "{S}im{CLS}: A Simple Framework for Contrastive Learning of Abstractive Summarization", |
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author = "Liu, Yixin and |
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Liu, Pengfei", |
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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)", |
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month = aug, |
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year = "2021", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2021.acl-short.135", |
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doi = "10.18653/v1/2021.acl-short.135", |
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pages = "1065--1072", |
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} |
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``` |
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