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license: cc-by-4.0 |
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This is model based on mT5-L that predicts a binary label for a given article and summary for Q6 (conciseness), as defined in the [SEAHORSE paper](https://arxiv.org/abs/2305.13194) (Clark et al., 2023). |
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It is trained similarly to the [TRUE paper (Honovich et al, 2022)](https://arxiv.org/pdf/2204.04991.pdf) on human ratings from the SEAHORSE dataset in 6 languages: |
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- German |
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- English |
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- Spanish |
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- Russian |
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- Turkish |
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- Vietnamese |
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The input format for the model is: "premise: ARTICLE hypothesis: SUMMARY", where ARTICLE is the document being summarized and SUMMARY is the candidate summary. |
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There is also an XXL version of this model, as well as metrics trained for each of the other 5 dimensions described in the original paper. |
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The full citation for the SEAHORSE paper is: |
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``` |
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@misc{clark2023seahorse, |
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title={SEAHORSE: A Multilingual, Multifaceted Dataset for Summarization Evaluation}, |
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author={Elizabeth Clark and Shruti Rijhwani and Sebastian Gehrmann and Joshua Maynez and Roee Aharoni and Vitaly Nikolaev and Thibault Sellam and Aditya Siddhant and Dipanjan Das and Ankur P. Parikh}, |
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year={2023}, |
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eprint={2305.13194}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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Contact: seahorse-authors@google.com |
