Abstract
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/.
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CITATION STYLE
Syed, S., Yousef, T., Al-Khatib, K., Jänicke, S., & Potthast, M. (2021). SUMMARY EXPLORER Visualizing the State of the Art in Text Summarization. In EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations (pp. 185–194). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.emnlp-demo.22
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