Summary Explorer: Visualizing the State of the Art in Text Summarization
This tool addresses the problem of evaluating summarization models for researchers and practitioners, though it is incremental as it builds on existing debugging approaches.
The paper tackles the challenge of manually inspecting text summarization systems by introducing Summary Explorer, a tool that compiles outputs from 55 state-of-the-art approaches on three benchmark datasets and enables visual exploration for qualitative assessment.
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/