T ab G enie: A Toolkit for Table-to-Text Generation (2023)

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 https://github.com/kasnerz/tabgenie .

## introduction building and evaluating data-to-text (d2t) generation systems @xcite requires understanding the data and observing system behavior. it is, however, not trivial to interact with the large volume of d2t generation datasets that have emerged in the last years (see a key component missing from current d2t tools is the possibility to visualize the input data and generated outputs. visualization plays an important role in examining and evaluating scientific data @xcite and can help d2t generation researchers to make more informed de- along with that, demands for a unified input data format have recently been raised with multi-task training for large language models (llms) @xcite @xcite . some works have used simple data linearization techniques for converting structured data to a textual format, in order to align it with the format used for other tasks @xcite . however, linearizations are using custom preprocessing code, leading to discrepancies between individual works. in this paper, we present tabgenie -a multipurpose toolkit for interacting with d2t generation datasets and systems designed to fill these gaps. on a high level, the toolkit consists of (a) an interactive web interface, (b) a set of command-line processing tools, and (c) a set of python bindings (see figure 1 ). ## data we currently include 16 datasets listed in ## web interface tabgenie offers a user-friendly way to interact with table-to-text generation datasets through the web interface. the interface can be rendered using a local server (cf. §4.2) and can be viewed in any modern web browser. the interface features a simple, single-page layout, which contains a navigation bar and three panels containing user controls, input data, and system outputs (see figure 2 ). although the interface primarily aims at researchers, it can be also used by non-expert users. ## developer tools tabgenie also provides a developer-friendly interface: python bindings ( §4.1) and a commandline interface ( §4.2). both of these interfaces aim to simplify dataset preprocessing in downstream tasks. the key benefit of using tabgenie is that it provides streamlined access to data in a consistent format, removing the need for dataset-specific code 4 https://jsonlines.org for extracting information such as table properties, references, or individual cell values. ## case studies in this section, we outline several recipes for using tabgenie in d2t generation research. the instructions and code samples for these tasks are available in the project repository. ## conclusion we presented tabgenie, a multifunctional software package for table-to-text generation. tab-genie bridges several gaps including visualizing input data, unified data access, and interactive tableto-text generation. as such, tabgenie provides a comprehensive set of tools poised to accelerate progress in the field of d2t generation.