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        <title>SUMMARY WORKBENCH: Unifying Application and Evaluation of Text Summarization Models</title>
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        <namePart type="given">Shahbaz</namePart>
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            <title>Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations</title>
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            <namePart type="given">Wanxiang</namePart>
            <namePart type="family">Che</namePart>
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            <namePart type="given">Ekaterina</namePart>
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    <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 https://tldr.demo.webis.de and also supports local deployment for private resources.</abstract>
    <identifier type="citekey">syed-etal-2022-summary</identifier>
    <identifier type="doi">10.18653/v1/2022.emnlp-demos.23</identifier>
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%0 Conference Proceedings
%T SUMMARY WORKBENCH: Unifying Application and Evaluation of Text Summarization Models
%A Syed, Shahbaz
%A Schwabe, Dominik
%A Potthast, Martin
%Y Che, Wanxiang
%Y Shutova, Ekaterina
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F syed-etal-2022-summary
%X 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 https://tldr.demo.webis.de and also supports local deployment for private resources.
%R 10.18653/v1/2022.emnlp-demos.23
%U https://aclanthology.org/2022.emnlp-demos.23/
%U https://doi.org/10.18653/v1/2022.emnlp-demos.23
%P 232-241

Markdown (Informal)

[SUMMARY WORKBENCH: Unifying Application and Evaluation of Text Summarization Models](https://aclanthology.org/2022.emnlp-demos.23/) (Syed et al., EMNLP 2022)

• SUMMARY WORKBENCH: Unifying Application and Evaluation of Text Summarization Models (Syed et al., EMNLP 2022)

ACL

• Shahbaz Syed, Dominik Schwabe, and Martin Potthast. 2022. SUMMARY WORKBENCH: Unifying Application and Evaluation of Text Summarization Models. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 232–241, Abu Dhabi, UAE. Association for Computational Linguistics.