@inproceedings{syed-etal-2022-summary,
    title = "{SUMMARY} {WORKBENCH}: Unifying Application and Evaluation of Text Summarization Models",
    author = "Syed, Shahbaz  and
      Schwabe, Dominik  and
      Potthast, Martin",
    editor = "Che, Wanxiang  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.emnlp-demos.23/",
    doi = "10.18653/v1/2022.emnlp-demos.23",
    pages = "232--241",
    abstract = "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."
}