| Challenge: | Automatic Text Simplification (ATS) is a major natural language processing task that aims to help people understand complex text. |
| Approach: | They propose to use a human-annotated dataset to study automatic text simplification models to determine which metrics to use when evaluating new models. |
| Outcome: | The proposed models reconstruct the text into a simpler format by deletion, substitution, addition or splitting, while preserving the original meaning and correct grammar. |
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Adapting Sentence-level Automatic Metrics for Document-level Simplification Evaluation (2025.naacl-long)
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| Challenge: | Existing studies on text simplification have focused on sentence simplification, but these metrics often underperform on longer texts. |
| Approach: | They propose to adapt existing sentence-level metrics for paragraph- or document-level simplification by incorporating a new approach to the evaluation of text simplification metrics. |
| Outcome: | The proposed approach outperforms existing sentence-level metrics in terms of correlation with human judgment and the sensitivity and robustness of various metrics to different types of errors produced by existing systems. |
Document-Level Text Simplification: Dataset, Criteria and Baseline (2021.emnlp-main)
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| Challenge: | Text simplification is a valuable technique, but research on it is limited. |
| Approach: | They propose a document-level simplification task using Wikipedia dumps as a dataset and propose an automatic evaluation metric called D-SARI. |
| Outcome: | The proposed metric is more suitable for document-level simplification task. |
Linguistic Corpus Annotation for Automatic Text Simplification Evaluation (2022.emnlp-main)
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Rémi Cardon, Adrien Bibal, Rodrigo Wilkens, David Alfter, Magali Norré, Adeline Müller, Watrin Patrick, Thomas François
| Challenge: | Evaluating automatic text simplification systems is a difficult task that is performed either by automatic metrics or user-based evaluations. |
| Approach: | They propose to use annotations of the ASSET corpus to analyze SARI’s behavior and to re-evaluate existing ATS systems. |
| Outcome: | The proposed methods can be used to analyze SARI’s behavior and to re-evaluate existing ATS systems. |
AutoMeTS: The Autocomplete for Medical Text Simplification (2020.coling-main)
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| Challenge: | Semi-automated text simplification approaches can be used to simplify text faster and at a higher quality. |
| Approach: | They propose to use autocomplete to simplify medical texts using aligned English Wikipedia sentences and pretrained neural language models to analyze the additional context. |
| Outcome: | The proposed model outperforms the best individual model by 2.1% and achieves a word prediction accuracy of 64.52%. |
A Detailed Evaluation of Neural Sequence-to-Sequence Models for In-domain and Cross-domain Text Simplification (L18-1)
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| Challenge: | Xu et al., 2016) show that a simple neural architecture can be efficiently used for in-domain and cross-domain text simplification. |
| Approach: | They evaluate neural sequence-to-sequence models for text simplification on Wikipedia and Newsela datasets. |
| Outcome: | The proposed model can generalize across corpora and overcome challenges when tested on Wikipedia and Newsela datasets. |
DETECT: Determining Ease and Textual Clarity of German Text Simplifications (2026.eacl-long)
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| Challenge: | Current evaluation of German automatic text simplification relies on general-purpose metrics such as SARI, BLEU, and BERTScore. |
| Approach: | They propose a German-specific metric that holistically evaluates ATS quality across all three dimensions of simplicity, meaning preservation, and fluency. |
| Outcome: | The proposed metric achieves higher correlations with human judgments than widely used ATS metrics. |
Revisiting non-English Text Simplification: A Unified Multilingual Benchmark (2023.acl-long)
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| Challenge: | Recent advances in English automatic text simplification have pushed the frontier of multilingual text simulating. |
| Approach: | They propose to use multilingual evaluation benchmarks to evaluate multilingual text simplification models in English and other languages. |
| Outcome: | The proposed benchmark outperforms pre-trained models in Russian in zero-shot cross-lingual transfer to low-resource languages. |
Simplicity Level Estimate (SLE): A Learned Reference-Less Metric for Sentence Simplification (2023.emnlp-main)
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| Challenge: | Existing evaluation metrics conflate simplicity with correlated attributes such as fluency or meaning preservation. |
| Approach: | They propose a new learning evaluation metric that focuses on simplicity outperforming most existing metrics in terms of correlation with human judgements. |
| Outcome: | The proposed metric outperforms most existing metrics in terms of correlation with human judgements. |
Text Simplification from Professionally Produced Corpora (L18-1)
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| Challenge: | Existing approaches to Text Simplification rely on the Wikipedia-Simple Wikipedia parallel corpus, which is used for many tasks. |
| Approach: | They propose to use the Newsela corpus to extract 550, 644 complex-simple sentence pairs from the corpus and introduce a lexical simplifier that uses the corpu to generate candidate simplifications. |
| Outcome: | The proposed model outperforms state-of-the-art approaches and generates candidate simplifications from the newsela corpus. |
BLESS: Benchmarking Large Language Models on Sentence Simplification (2023.emnlp-main)
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Tannon Kew, Alison Chi, Laura Vásquez-Rodríguez, Sweta Agrawal, Dennis Aumiller, Fernando Alva-Manchego, Matthew Shardlow
| Challenge: | BLESS is a performance benchmark of the most recent state-of-the-art Large Language Models (LLMs) on the task of text simplification (TS). |
| Approach: | They present a performance benchmark of the most recent state-of-the-art Large Language Models (LLMs) on the task of text simplification (TS). |
| Outcome: | The proposed benchmarks show that the most recent state-of-the-art LLMs perform better on the task of text simplification (TS). |