This paper presents Summary Workbench, a new tool for developing and evaluating text summarization models. New models and evaluation measures can be easily integrated as Docker-based plugins, allowing to examine the quality of their summaries against any input and to evaluate them using various evaluation measures. Visual analyses combining multiple measures provide insights into the models' strengths and weaknesses. The tool is hosted at \url{https://tldr.demo.webis.de} and also supports local deployment for private resources.
@article{arxiv.2210.09587,
title = {Summary Workbench: Unifying Application and Evaluation of Text Summarization Models},
author = {Shahbaz Syed and Dominik Schwabe and Martin Potthast},
journal= {arXiv preprint arXiv:2210.09587},
year = {2022}
}