Summary Explorer: Visualizing the State of the Art in Text Summarization

Shahbaz Syed, Tariq Yousef, Khalid Al Khatib, Stefan Jänicke, Martin Potthast


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/
Anthology ID:
2021.emnlp-demo.22
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
185–194
Language:
URL:
https://aclanthology.org/2021.emnlp-demo.22
DOI:
10.18653/v1/2021.emnlp-demo.22
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-demo.22.pdf
Code
 webis-de/summary-explorer