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# Dataset Card for SemTabNet
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This dataset accompanies the following paper:
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Title: Statements: Universal Information Extraction from Tables with Large Language Models for ESG KPIs
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In this paper, we propose **STATEMENTS** as a new knowledge model for storing quantiative information in a domain agnotic, uniform structure. The task of converting a raw input (table or text) to Statements is called Statement Extraction (SE). The statement extraction task falls under the category of universal information extraction.
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- **Code Repository:** [SemTabNet repository](https://github.com/DS4SD/SemTabNet)
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- **Arxiv Paper:** [Statements: Universal Information Extraction from Tables with Large Language Models for ESG KPIs]()
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- **Point of Contact:** [IBM Research DeepSearch Team](https://ds4sd.github.io)
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### Citation Information
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# Dataset Card for SemTabNet
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This dataset accompanies the following [paper](https://arxiv.org/abs/2406.19102):
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Title: Statements: Universal Information Extraction from Tables with Large Language Models for ESG KPIs
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In this paper, we propose **STATEMENTS** as a new knowledge model for storing quantiative information in a domain agnotic, uniform structure. The task of converting a raw input (table or text) to Statements is called Statement Extraction (SE). The statement extraction task falls under the category of universal information extraction.
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- **Code Repository:** [SemTabNet repository](https://github.com/DS4SD/SemTabNet)
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- **Arxiv Paper:** [Statements: Universal Information Extraction from Tables with Large Language Models for ESG KPIs](https://arxiv.org/abs/2406.19102)
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- **Point of Contact:** [IBM Research DeepSearch Team](https://ds4sd.github.io)
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### Citation Information
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Arxiv: [https://arxiv.org/abs/2406.19102](https://arxiv.org/abs/2406.19102)
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