| 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. |
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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. |
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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. |
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Meta-Evaluation of Sentence Simplification Metrics (2024.lrec-main)
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| 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. |
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ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations (2020.acl-main)
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| Challenge: | Existing models for sentence simplification are focused on a single transformation, such as lexical paraphrasing or splitting. |
| Approach: | They propose a dataset for assessing sentence simplification in English using a crowdsourced multi-reference corpus. |
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Evaluating LLMs for Portuguese Sentence Simplification with Linguistic Insights (2025.acl-long)
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Arthur Mariano Rocha De Azevedo Scalercio, Elvis A. De Souza, Maria José Bocorny Finatto, Aline Paes
| Challenge: | Sentence simplification (SS) aims to make sentences more straightforward to read and understand without changing its key points. |
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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). |
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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. |
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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. |
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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. |
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. |