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

arXiv: https://arxiv.org/abs/2108.01879 · HTML: https://mlsanity.com/paper/2108.01879/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 45 references that are in the map.
- First posted on arXiv 4 Aug 2021.

## Builds on (most cited first, 23 of 23)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://mlsanity.com/paper/1808.08745/) (2018)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [On Faithfulness and Factuality in Abstractive Summarization](https://mlsanity.com/paper/2005.00661/) (2020)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [Evaluating the Factual Consistency of Abstractive Text Summarization](https://mlsanity.com/paper/1910.12840/) (2019)
- [Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies](https://mlsanity.com/paper/1804.11283/) (2018)
- [FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization](https://mlsanity.com/paper/2005.03754/) (2020)
- [Neural Text Summarization: A Critical Evaluation](https://mlsanity.com/paper/1908.08960/) (2019)
- [Bottom-Up Abstractive Summarization](https://mlsanity.com/paper/1808.10792/) (2018)
- [Re-evaluating Evaluation in Text Summarization](https://mlsanity.com/paper/2010.07100/) (2020)
- [Faithful to the Original: Fact Aware Neural Abstractive Summarization](https://mlsanity.com/paper/1711.04434/) (2017)
- [The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models](https://mlsanity.com/paper/2008.05122/) (2020)
- [Entity-level Factual Consistency of Abstractive Text Summarization](https://mlsanity.com/paper/2102.09130/) (2021)
- [Better Summarization Evaluation with Word Embeddings for ROUGE](https://mlsanity.com/paper/1508.06034/) (2015)
- [Content Selection in Deep Learning Models of Summarization](https://mlsanity.com/paper/1810.12343/) (2018)
- [BanditSum: Extractive Summarization as a Contextual Bandit](https://mlsanity.com/paper/1809.09672/) (2018)
- [Improving Abstraction in Text Summarization](https://mlsanity.com/paper/1808.07913/) (2018)
- [Scoring Sentence Singletons and Pairs for Abstractive Summarization](https://mlsanity.com/paper/1906.00077/) (2019)
- [Reducing Quantity Hallucinations in Abstractive Summarization](https://mlsanity.com/paper/2009.13312/) (2020)
- [HighRES: Highlight-based Reference-less Evaluation of Summarization](https://mlsanity.com/paper/1906.01361/) (2019)
- [SummVis: Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarization](https://mlsanity.com/paper/2104.07605/) (2021)
- [VizSeq: A Visual Analysis Toolkit for Text Generation Tasks](https://mlsanity.com/paper/1909.05424/) (2019)

## Built on by (most cited first, 4 of 4)

- [Text-Blueprint: An Interactive Platform for Plan-based Conditional Generation](https://mlsanity.com/paper/2305.00034/) (2023)
- [Source Identification in Abstractive Summarization](https://mlsanity.com/paper/2402.04677/) (2024)
- [Multi-Review Fusion-in-Context](https://mlsanity.com/paper/2403.15351/) (2024)
- [Summary Workbench: Unifying Application and Evaluation of Text Summarization Models](https://mlsanity.com/paper/2210.09587/) (2022)

## 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/