Papers with SARI
EASSE: Easier Automatic Sentence Simplification Evaluation (D19-3)
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| Challenge: | EASSE provides access to a broad range of evaluation resources including standard automatic metrics, word-level accuracy scores and reference-independent quality estimation features. |
| Approach: | They propose to provide a Python package that provides access to automatic evaluation and comparison of Sentence Simplification (SS) systems. |
| Outcome: | The proposed tool allows comparison and understanding of the performance of Sentence Simplification (SS) systems. |
Controllable Text Simplification with Lexical Constraint Loss (P19-2)
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| Challenge: | Existing models that only consider the sentence level generate words beyond the target level. |
| Approach: | They propose a method to control the level of a sentence in a text simplification task . they add the target grade level as input and weight words in the loss function . |
| Outcome: | The proposed method improves both BLEU and SARI scores and achieves aggressive rewriting. |
Dynamic Multi-Level Multi-Task Learning for Sentence Simplification (C18-1)
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| Challenge: | Sentence simplification is the task of improving readability and understandability of an input text. |
| Approach: | They propose a strong pointer-copy mechanism based sequence-to-sequence sentence simplification model and a novel ‘multi-level’ soft sharing approach where each auxiliary task shares different (higher versus lower) level layers of the model. |
| Outcome: | The proposed model outperforms competing simplification systems in SARI and FKGL automatic metrics, and human evaluation. |
LAMP-MedQA: A Lightweight Multi-Agent System for Patient-Oriented Medical Question Answering (2026.acl-srw)
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| Challenge: | Large language models (LLMs) are a promising way to bridge the gap between patient health literacy and access to care. |
| Approach: | They evaluate a range of open- and closed-source LLMs on a MeDiSumQA dataset . they propose a lightweight multi-agent framework for patient-oriented medical question answering . |
| Outcome: | The proposed model achieves lower FKGL than zero-shot GPT-5 and highest simplification quality among all models. |
French Biomedical Text Simplification: When Small and Precise Helps (2020.coling-main)
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| Challenge: | Existing studies on text simplification in English use large parallel monolingual corpora in which one complex sentence is paired with one or more simplified versions. |
| Approach: | They use parallel sentences from existing health comparable corpora in French and WikiLarge corpus translated from English to French and a lexicon that associates medical terms with paraphrases. |
| Outcome: | The proposed models are based on sentences from existing health comparable corpora in French and WikiLarge corpus translated from English to French. |
Investigating Text Simplification Evaluation (2021.findings-acl)
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| Challenge: | Existing studies show that parallel TS corpora contain inaccurate simplifications and incorrect alignments. |
| Approach: | They propose to improve the distribution of parallel text simplification corpora to build more robust TS models. |
| Outcome: | The proposed models can be improved by improving the distribution of TS datasets. |
NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization (2023.findings-eacl)
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| Challenge: | a recent study shows that accessing medical literature is difficult for laypeople because it is written for specialists and contains medical jargon. |
| Approach: | They propose a two-stage strategy to identify relevant content to be simplified . they first generate reference summaries via sentence matching between the original and simplified abstracts . |
| Outcome: | The proposed approach improves on a seq2seq-based test set on an English medical corpus . it also improves the SARI score by 1.1% . |
Leveraging Prefix Transfer for Multi-Intent Text Revision (2023.acl-short)
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Ruining Chong, Cunliang Kong, Liu Wu, Zhenghao Liu, Ziye Jin, Liner Yang, Yange Fan, Hanghang Fan, Erhong Yang
| Challenge: | Text revision is a necessary process to improve text quality. |
| Approach: | They propose a multi-intent text revision system that can revise texts without explicit intent annotation. |
| Outcome: | The proposed system outperforms baselines on the IteraTeR dataset and significantly improves the SARI score with more than 3% improvement. |
Learning Simplifications for Specific Target Audiences (P18-2)
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| Challenge: | Text simplification is a monolingual text-to-text transformation task . data from TS data can contain multiple simplifications of the same original text . |
| Approach: | They propose to use sequence-to-sequence neural models to build models tailored for specific grade levels. |
| Outcome: | The proposed model outperforms state-of-the-art approaches for a monolingual text-to-text transformation task. |
On the Helpfulness of Document Context to Sentence Simplification (2020.coling-main)
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| Challenge: | Text simplification is a hot issue in the field of natural language generation (NLG). |
| Approach: | They propose to use Wikipedia context to improve sentence simplification by using neural networks to learn the effects of preceding and following sentences on current sentences. |
| Outcome: | The proposed model outperforms the best performing model on the baseline dataset by 2.46 (7.22%). |
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. |
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. |
Mask-to-Correct+: Leveraging Retriever Diversity for Masking-guided Faithful Fact Correction (2026.acl-long)
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| Challenge: | Existing methods for fact correction ignore semantic faithfulness in their process. |
| Approach: | They propose a supervised learning approach that uses a diversity-aware masking approach to identify erroneous spans of claims and evaluate the faithfulness of corrections using retrieved evidence. |
| Outcome: | The proposed framework outperforms baseline frameworks on social media datasets, achieving up to 14% improvement in SARI scores, without using gold evidence. |
Evidence-based Factual Error Correction (2021.acl-long)
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| Challenge: | Existing methods to correct factual errors are limited to labeled claims . a recent task of fact verification has attracted significant attention . |
| Approach: | They propose a task of factual error correction that performs edits to a claim so that the generated rewrite is better supported by evidence. |
| Outcome: | The proposed method produces accurate factual error corrections for 5x more instances in human evaluation and a .125 increase in SARI score. |
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. |
EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing (P19-1)
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| Challenge: | Current sentence simplification systems are variants of sequence-to-sequence models adopted from machine translation. |
| Approach: | They propose a sentence simplification model that learns explicit edit operations via a neural programmer-interpreter approach. |
| Outcome: | The proposed model outperforms state-of-the-art models on three benchmark text simplification corpora in terms of SARI (+0.95 WikiLarge, +1.89 WikiSmall, -1.41 Newsela) |
Controllable Sentence Simplification in Swedish Using Control Prefixes and Mined Paraphrases (2024.lrec-main)
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| Challenge: | Automated Text Simplification (ATS) systems aim to facilitate readability and comprehension by reducing linguistic complexity. |
| Approach: | They propose to use a dataset of Swedish paraphrases to train ATS models utilizing prefix-tuning with control prefixes to provide more control over the simplification. |
| Outcome: | The proposed model improves on the baseline model and compares with previous models. |
Text Simplification via Adaptive Teaching (2024.findings-acl)
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| Challenge: | Text simplification is the process of rewriting a text using simpler vocabulary and grammatical structure in order to make it more accessible and understandable for a larger audience. |
| Approach: | They propose a model for text simplification based on adaptive teaching using a teacher network and a text generation network. |
| Outcome: | The proposed model outperforms the current state-of-the-art model on the Wiki-Doc and D-Wikipedia datasets and performs well on human evaluations in terms of text simplicity, correctness, and fluency. |
Multitasking Framework for Unsupervised Simple Definition Generation (2022.acl-long)
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| Challenge: | Existing definition generation tasks require a dictionary with complex definitions and a corpus containing arbitrary simple texts to generate them. |
| Approach: | They propose a multitasking framework SimpDefiner that only requires a standard dictionary with complex definitions and a corpus containing arbitrary simple texts. |
| Outcome: | The proposed framework outperforms the baseline model by a 1.77 SARI score on the English dataset, and raises the proportion of the low level (HSK level 1-3) words in Chinese definitions by 3.87%. |
SimplifyUR: Unsupervised Lexical Text Simplification for Urdu (2020.lrec-1)
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| Challenge: | Existing methods for text simplification for Urdu rely on manual lexicons and simplified corpora, but are not applicable to the language. |
| Approach: | They propose an unsupervised method for automatic text simplification for Urdu using word embeddings and morphological features. |
| Outcome: | The proposed method achieves BLEU score of 80.15 and SARI score of 42.02 on simple text generated on simplified corpora and human evaluations for correctness, grammaticality, meaning-preservation and simplicity. |
Keep It Simple: Unsupervised Simplification of Multi-Paragraph Text (2021.acl-long)
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| Challenge: | a novel approach to text simplification learns to balance a reward across three properties: fluency, salience and simplicity. |
| Approach: | They propose a novel algorithm to optimize the reward which proposes several candidate simplifications and a realistic text comprehension task as an evaluation method for text simplification. |
| Outcome: | The proposed model outperforms strong supervised baselines on the English news domain and can help people complete a comprehension task an average of 18% faster while retaining accuracy. |
SIMSUM: Document-level Text Simplification via Simultaneous Summarization (2023.acl-long)
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| Challenge: | Document-level text simplification is a specific type of simplification which involves simplifying documents consisting of several sentences by rewriting them into fewer or more sentences. |
| Approach: | They propose a new two-stage framework SIMSUM for automated document-level text simplification which uses explicit summarization and simplification models and guides the generation using the main keywords of a source text. |
| Outcome: | The proposed model outperforms baseline models on two document-level simplification datasets, namely D-Wikipedia and Wiki-Doc. |
Controllable Sentence Simplification (2020.lrec-1)
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| Challenge: | Text simplification is often considered an all-purpose generic task where the same simplifications are suitable for all but multiple audiences can benefit from simplified text in different ways. |
| Approach: | They propose a controllable simplification model that provides explicit control on simplification systems based on Sequence-to-Sequence models. |
| Outcome: | The proposed model outperforms standard models on simplification benchmarks. |
PivotFEC: Enhancing Few-shot Factual Error Correction with a Pivot Task Approach using Large Language Models (2023.findings-emnlp)
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| Challenge: | Existing methods for Factual Error Correction (FEC) use mask-then-correct paradigms . however, the lack of datasets containing false claims has impeded progress . |
| Approach: | They propose a method that enhances few-shot FEC with a pivot task approach using large language models. |
| Outcome: | The proposed method outperforms its few-shot counterpart by 7.9 points in SARI . it improves widely-adopted SARI metrics by 11.3 compared to the best-performing methods . |
Replace, Paraphrase or Fine-tune? Evaluating Automatic Simplification for Medical Texts in Spanish (2024.lrec-main)
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Leonardo Campillos-Llanos, Ana Rosa Terroba, Rocío Bartolomé, Ana Valverde-Mateos, Cristina González, Adrián Capllonch-Carrión, Jonathan Heras
| Challenge: | lexicon-based simplification methods can help patients understand medical documents . but they must ensure that the content is transmitted rigorously and not creating wrong information. |
| Approach: | They tested automatic simplification techniques using a Spanish lexicon of technical and laymen terms. |
| Outcome: | The proposed methods improve the quantitative results and the human evaluation of medical documents. |
SENTA: Sentence Simplification System for Slovene (2024.lrec-main)
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| Challenge: | Sentence simplification involves converting complex sentences into more accessible forms while preserving their meaning and context. |
| Approach: | They propose a system for sentence simplification in Slovene that uses a neural classifier to identify sentences that need simplification and a large Slovenen language model to refine sentences into a simpler form. |
| Outcome: | The proposed system achieves an excellent SARI score of 41 for a large Slovene language model based on T5 architecture . it is integrated into a freely accessible, user-friendly user interface, offering a valuable service to less-fluent Slovenen users. |