  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 the inputs are represented as tables with associated metadata.
The tables can be explored through the 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 https://github.com/kasnerz/tabgenie.
