@inproceedings{syed-etal-2021-summary,
title = "Summary Explorer: Visualizing the State of the Art in Text Summarization",
author = {Syed, Shahbaz and
Yousef, Tariq and
Al Khatib, Khalid and
J{\"a}nicke, Stefan and
Potthast, Martin},
editor = "Adel, Heike and
Shi, Shuming",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-demo.22",
doi = "10.18653/v1/2021.emnlp-demo.22",
pages = "185--194",
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 \url{https://tldr.webis.de/}",
}
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<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/</abstract>
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%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
Markdown (Informal)
[Summary Explorer: Visualizing the State of the Art in Text Summarization](https://aclanthology.org/2021.emnlp-demo.22) (Syed et al., EMNLP 2021)
ACL
- Shahbaz Syed, Tariq Yousef, Khalid Al Khatib, Stefan Jänicke, and Martin Potthast. 2021. Summary Explorer: Visualizing the State of the Art in Text Summarization. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 185–194, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.