Summary Explorer: Visualizing the State of the Art in Text Summarization
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Abstract (arXiv)
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/
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 23 of its 45 references that are in the map. First posted on arXiv 4 Aug 2021.
Builds on
23 of 45 references.
References · 23
- 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
- 2018Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarizationcited by 1,308
- 2015Teaching Machines to Read and Comprehendcited by 1,039
- 2020On Faithfulness and Factuality in Abstractive Summarizationcited by 895
- 2016Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyondcited by 809
- 2019Evaluating the Factual Consistency of Abstractive Text Summarizationcited by 578
- 2018Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategiescited by 292
- 2020FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarizationcited by 292
- 2019Neural Text Summarization: A Critical Evaluationcited by 189
- 2018Bottom-Up Abstractive Summarizationcited by 177
- 2020Re-evaluating Evaluation in Text Summarizationcited by 144
- 2017Faithful to the Original: Fact Aware Neural Abstractive Summarizationcited by 141
11 more
- 2020The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Modelscited by 125
- 2021Entity-level Factual Consistency of Abstractive Text Summarizationcited by 118
- 2015Better Summarization Evaluation with Word Embeddings for ROUGEcited by 66
- 2018Content Selection in Deep Learning Models of Summarizationcited by 64
- 2018BanditSum: Extractive Summarization as a Contextual Banditcited by 46
- 2018Improving Abstraction in Text Summarizationcited by 35
- 2019Scoring Sentence Singletons and Pairs for Abstractive Summarizationcited by 34
- 2020Reducing Quantity Hallucinations in Abstractive Summarizationcited by 22
- 2019HighRES: Highlight-based Reference-less Evaluation of Summarizationcited by 21
- 2021SummVis: Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarizationcited by 13
- 2019VizSeq: A Visual Analysis Toolkit for Text Generation Taskscited by 10
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Citing papers per year: 2022: 1; 2023: 1; 2024: 2.
4 papers.