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--- |
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library_name: transformers |
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language: |
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- en |
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license: |
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- cc-by-nc-sa-4.0 |
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--- |
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# Model Card: T5-base-summarization-claim-extractor |
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## Model Description |
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**Model Name:** T5-base-summarization-claim-extractor |
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**Authors:** Alessandro Scirè, Karim Ghonim, and Roberto Navigli |
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**Language:** English |
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**Primary Use:** Extraction of atomic claims from a summary |
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### Overview |
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The T5-base-summarization-claim-extractor is a model developed for the task of extracting atomic claims from summaries. The model is based on the T5 architecture which is then fine-tuned specifically for claim extraction. |
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This model was introduced as part of the research presented in the paper ["FENICE: Factuality Evaluation of summarization based on Natural Language Inference and Claim Extraction" by Alessandro Scirè, Karim Ghonim, and Roberto Navigli.](https://aclanthology.org/2024.findings-acl.841.pdf) FENICE leverages Natural Language Inference (NLI) and Claim Extraction to evaluate the factuality of summaries. |
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### Intended Use |
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This model is designed to: |
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- Extract atomic claims from summaries. |
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- Serve as a component in pipelines for factuality evaluation of summaries. |
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## Example Code |
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You can use the following code to perform operations such as getting distinct elements from a list or splitting text into sentences. |
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```python |
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from transformers import T5ForConditionalGeneration, T5Tokenizer |
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tokenizer = T5Tokenizer.from_pretrained("Babelscape/t5-base-summarization-claim-extractor") |
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model = T5ForConditionalGeneration.from_pretrained("Babelscape/t5-base-summarization-claim-extractor").to("cuda:0") |
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device = "cuda:0" |
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summary = 'Simone Biles made a triumphant return to the Olympic stage at the Paris 2024 Games, competing in the women’s gymnastics qualifications. Overcoming a previous struggle with the “twisties” that led to her withdrawal from events at the Tokyo 2020 Olympics, Biles dazzled with strong performances on all apparatus, helping the U.S. team secure a commanding lead in the qualifications. Her routines showcased her resilience and skill, drawing enthusiastic support from a star-studded audience' |
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tok_input = tokenizer.batch_encode_plus([summary], return_tensors="pt", padding=True).to(device) |
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claims = model.generate(**tok_input) |
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claims = tokenizer.batch_decode(claims, skip_special_tokens=True) |
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``` |
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### Training |
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For details regarding the training process, please checkout our [paper](https://aclanthology.org/2024.findings-acl.841.pdf) (section 4.1). |
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### Performance |
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| <div style="width: 250px">Model</div> | easiness<sub>P</sub> | easiness<sub>R</sub> | easiness<sub>F1</sub> | |
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|:-------------------------------------:|:--------------------:|:--------------------:|:---------------------:| |
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| GPT-3.5 | 80.1 | 70.9 | 74.9 | |
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| t5-base-summarization-claim-extractor | 79.2 | 68.8 | 73.4 | |
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**Table 1:** Easiness Precision (easiness<sub>P</sub>), Recall (easiness<sub>R</sub>), and F1 score (easiness<sub>F1</sub>) results for the LLM-based claim extractor, namely **GPT-3.5**, and **t5-base-summarization-claim-extractor**, assessed on [ROSE (Liu et al., 2023b)](https://aclanthology.org/2023.acl-long.228/). |
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Further details on the model's performance and the metrics used can be found in the [paper](https://aclanthology.org/2024.findings-acl.841.pdf) (section 4.1). |
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### Main Repository |
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For more details about FENICE, check out the GitHub repository: |
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[Babelscape/FENICE](https://github.com/Babelscape/FENICE) |
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### Citation |
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If you use this model in your work, please cite the following paper: |
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```bibtex |
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@inproceedings{scire-etal-2024-fenice, |
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title = "{FENICE}: Factuality Evaluation of summarization based on Natural language Inference and Claim Extraction", |
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author = "Scir{\`e}, Alessandro and Ghonim, Karim and Navigli, Roberto", |
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editor = "Ku, Lun-Wei and Martins, Andre and Srikumar, Vivek", |
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booktitle = "Findings of the Association for Computational Linguistics ACL 2024", |
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month = aug, |
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year = "2024", |
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address = "Bangkok, Thailand and virtual meeting", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2024.findings-acl.841", |
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pages = "14148--14161", |
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} |
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``` |
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### Limitations |
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- The model is specifically designed for extracting claims from summaries and may not perform well on other types of texts. |
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- The model is currently available only in English and may not generalize well to other languages. |
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### Ethical Considerations |
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Users should be aware that while this model extracts claims that can be evaluated for factuality, it does not determine the truthfulness of those claims. Therefore, it should be used in conjunction with other tools or human judgment when evaluating the reliability of summaries. |
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### Acknowledgments |
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This work was made possible thanks to the support of Babelscape and Sapienza NLP. |
