%0 Conference Proceedings %T Summary Explorer: Visualizing the State of the Art in Text Summarization %A Syed, Shahbaz %A Yousef, Tariq %A Al Khatib, Khalid %A Jänicke, Stefan %A Potthast, Martin %Y Adel, Heike %Y Shi, Shuming %S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations %D 2021 %8 November %I Association for Computational Linguistics %C Online and Punta Cana, Dominican Republic %F syed-etal-2021-summary %X This paper introduces Summary Explorer, a new tool to support the manual inspection of text summarization systems by compiling the outputs of 55 state-of-the-art single document summarization approaches on three benchmark datasets, and visually exploring them during a qualitative assessment. The underlying design of the tool considers three well-known summary quality criteria (coverage, faithfulness, and position bias), encapsulated in a guided assessment based on tailored visualizations. The tool complements existing approaches for locally debugging summarization models and improves upon them. The tool is available at https://tldr.webis.de/ %R 10.18653/v1/2021.emnlp-demo.22 %U https://aclanthology.org/2021.emnlp-demo.22 %U https://doi.org/10.18653/v1/2021.emnlp-demo.22 %P 185-194