Can summarization approximate simplification? A gold standard comparison
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Abstract (arXiv)
This study explores the overlap between text summarization and simplification outputs. While summarization evaluation methods are streamlined, simplification lacks cohesion, prompting the question: how closely can abstractive summarization resemble gold-standard simplification? We address this by applying two BART-based BRIO summarization methods to the Newsela corpus, comparing outputs with manually annotated simplifications and achieving a top ROUGE-L score of 0.654. This provides insight into where summarization and simplification outputs converge and differ.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 40 references that are in the map. First posted on arXiv 27 Jan 2025.
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References · 9
- 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
- 2014Sequence to Sequence Learning with Neural Networkscited by 2,986
- 2018Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarizationcited by 1,317
- 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,102
- 2015Teaching Machines to Read and Comprehendcited by 1,049
- 2022BRIO: Bringing Order to Abstractive Summarizationcited by 161
- 2017Sentence Simplification with Deep Reinforcement Learningcited by 109
- 2021How May I Help You? Using Neural Text Simplification to Improve Downstream NLP Taskscited by 9
- 2023SimpLex: a lexical text simplification architecturecited by 3
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