| Challenge: | TabGenie enables researchers to explore, preprocess, and analyze data-to-text generation datasets. |
| Approach: | They present TabGenie, a toolkit which enables researchers to explore, preprocess, and analyze a variety of data-to-text generation datasets. |
| Outcome: | The toolkit provides an interactive mode for debugging table-to-text generation, side-by-side comparison of generated system outputs, and easy exports for manual analysis. |
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| Challenge: | Recent studies have shown improvement in generating descriptive text from structured data. |
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QTSumm: Query-Focused Summarization over Tabular Data (2023.emnlp-main)
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| Challenge: | Existing table-to-text generation benchmarks have some limitations, such as E2E and ToTTo focusing on singlesentence generation tasks. |
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ToTTo: A Controlled Table-To-Text Generation Dataset (2020.emnlp-main)
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Ankur Parikh, Xuezhi Wang, Sebastian Gehrmann, Manaal Faruqui, Bhuwan Dhingra, Diyi Yang, Dipanjan Das
| Challenge: | Existing methods for data-to-text generation often hallucinate phrases not supported by the Wikipedia table. |
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