@inproceedings{kasner-etal-2023-tabgenie,
    title = "{T}ab{G}enie: A Toolkit for Table-to-Text Generation",
    author = "Kasner, Zden{\v{e}}k  and
      Garanina, Ekaterina  and
      Platek, Ondrej  and
      Dusek, Ondrej",
    editor = "Bollegala, Danushka  and
      Huang, Ruihong  and
      Ritter, Alan",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-demo.42",
    doi = "10.18653/v1/2023.acl-demo.42",
    pages = "444--455",
    abstract = "Heterogenity of data-to-text generation datasets limits the research on data-to-text generation systems. We present TabGenie {--} a toolkit which enables researchers to explore, preprocess, and analyze a variety of data-to-text generation datasets through the unified framework of table-to-text generation. In TabGenie, all inputs are represented as tables with associated metadata. The tables can be explored through a web interface, which also provides an interactive mode for debugging table-to-text generation, facilitates side-by-side comparison of generated system outputs, and allows easy exports for manual analysis. Furthermore, TabGenie is equipped with command line processing tools and Python bindings for unified dataset loading and processing. We release TabGenie as a PyPI package and provide its open-source code and a live demo at \url{https://github.com/kasnerz/tabgenie}.",
}

Heterogenity of data-to-text generation datasets limits the research on data-to-text generation systems. We present TabGenie {--} a toolkit which enables researchers to explore, preprocess, and analyze a variety of data-to-text generation datasets through the unified framework of table-to-text generation. In TabGenie, all inputs are represented as tables with associated metadata. The tables can be explored through a web interface, which also provides an interactive mode for debugging table-to-text generation, facilitates side-by-side comparison of generated system outputs, and allows easy exports for manual analysis. Furthermore, TabGenie is equipped with command line processing tools and Python bindings for unified dataset loading and processing. We release TabGenie as a PyPI package and provide its open-source code and a live demo at \url{https://github.com/kasnerz/tabgenie}.
Kasner, Zden{\v{e}}k Garanina, Ekaterina Platek, Ondrej Dusek, Ondrej
TabGenie: A Toolkit for Table-to-Text Generation