Papers with Text simplification
Parallel Text Alignment and Monolingual Parallel Corpus Creation from Philosophical Texts for Text Simplification (2021.naacl-srw)
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| Challenge: | Existing methods for text simplification require a lot of annotated data, however there are few suitable tools for this task. |
| Approach: | They propose an unsupervised method for aligning text based on Doc2Vec embeddings and an alignment algorithm capable of aligning texts at different levels. |
| Outcome: | The proposed method can be used to create a monolingual parallel corpus composed of the works of early modern philosophers and their corresponding simplified versions. |
Aligning Sentence Simplification with ESL Learner’s Proficiency for Language Acquisition (2025.naacl-long)
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| Challenge: | Text simplification is crucial for improving accessibility and comprehension for English as a Second Language (ESL) learners. |
| Approach: | They propose to simplify complex sentences to appropriate levels while also increasing vocabulary coverage of the target level. |
| Outcome: | The proposed method can increase frequency and diversity of vocabulary of the target level by more than 20% compared to baseline models, while maintaining high simplification quality. |
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. |
Explainable Prediction of Text Complexity: The Missing Preliminaries for Text Simplification (2021.acl-long)
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| Challenge: | Text simplification reduces the language complexity of professional content for accessibility purposes. |
| Approach: | They propose that text simplification can be decomposed into a pipeline of tasks . they show that the pipeline can be used to predict whether a text needs to be simplified . |
| Outcome: | The proposed model improves the performance of out-of-sample simplification tests on a blackbox lexical model . the proposed model reduces the complexity of professional text by a large margin . |
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. |
Benchmarking the Simplification of Dutch Municipal Text (2024.lrec-main)
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| Challenge: | Text simplification (TS) is a technique that makes written information more accessible to all people, especially those with cognitive or language impairments. |
| Approach: | They propose to use English as a pivot language for simplification of Dutch medical and municipal texts. |
| Outcome: | The proposed approach improves on Dutch medical text, while the existing pipeline performs better on all metrics. |
InfoLossQA: Characterizing and Recovering Information Loss in Text Simplification (2024.acl-long)
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Jan Trienes, Sebastian Joseph, Jörg Schlötterer, Christin Seifert, Kyle Lo, Wei Xu, Byron Wallace, Junyi Jessy Li
| Challenge: | Text simplification aims to make technical texts more accessible to laypeople but often results in deletion of information and vagueness. |
| Approach: | They propose a framework to characterize and recover simplification-induced information loss in form of question-and-answer (QA) pairs. |
| Outcome: | The proposed framework characterizes and recovers simplification-induced information loss in form of question-and-answer (QA) pairs. |
Medical Text Simplification: Optimizing for Readability with Unlikelihood Training and Reranked Beam Search Decoding (2023.findings-emnlp)
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| Challenge: | Text simplification has emerged as an increasingly useful application of AI for bridging the communication gap in specialized fields such as medicine, where the lexicon is often dominated by technical jargon and complex constructs. |
| Approach: | They propose a unlikelihood loss that encourages generation of simpler terms and a reranked beam search decoding method that optimizes for simplicity. |
| Outcome: | The proposed methods achieve better performance on readability metrics on three datasets. |
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. |
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. |
ExpertEase: A Multi-Agent Framework for Grade-Specific Document Simplification with Large Language Models (2024.findings-emnlp)
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| Challenge: | Existing studies mainly focus on sentence-level simplification, neglecting document-level and the different reading levels of target audiences. |
| Approach: | They propose a multi-agent framework for grade-specific document simplification using Large Language Models that integrates expert, teacher, and student agents that cooperate on the task and rely on external tools for calibration. |
| Outcome: | The proposed framework significantly improves the performance of large language models and compares them with human-authored texts. |
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. |
SWiPE: A Dataset for Document-Level Simplification of Wikipedia Pages (2023.acl-long)
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| Challenge: | Prior work on document-level simplification has focused on sentence-level edits, while many desirable edits require document- level context. |
| Approach: | They propose a dataset that reconstructs the document-level editing process from English Wikipedia to paired Simple Wikipedia articles. |
| Outcome: | The proposed dataset reconstructs the document-level editing process from English Wikipedia (EW) articles to paired Simple Wikipedia (SEW) pages. |
CWID-hi: A Dataset for Complex Word Identification in Hindi Text (2022.lrec-1)
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| Challenge: | Text simplification is a method for improving the accessibility of text by converting complex sentences into simple sentences. |
| Approach: | They propose to use Hindi knowledge annotators to capture the annotator’s language knowledge to build an automatic complex word classifier using a soft voting approach. |
| Outcome: | The proposed dataset shows that native and non-native annotators perceive complex words differently depending on their language knowledge. |
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. |
Is It Possible to Modify Text to a Target Readability Level? An Initial Investigation Using Zero-Shot Large Language Models (2024.lrec-main)
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| Challenge: | Text simplification and elaboration tasks are limited to only relatively altering the readability of texts to cater to a diverse audience. |
| Approach: | They propose to generate 8 versions of a text at different readability levels using ChatGPT and Llama-2 and introduce a two-step process to generate paraphrases. |
| Outcome: | The proposed task requires the generation of 8 versions at various target readability levels for each input text. |
Towards Reference-free Text Simplification Evaluation with a BERT Siamese Network Architecture (2023.findings-acl)
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| Challenge: | Text simplification (TS) aims to modify sentences to make their content and structure easier to understand. |
| Approach: | They propose a neural-network-based TS metric that uses a human reference to evaluate simplification and meaning preservation. |
| Outcome: | The proposed metric correlates better with human judgments for simplicity and meaning preservation than existing metrics. |
DEplain: A German Parallel Corpus with Intralingual Translations into Plain Language for Sentence and Document Simplification (2023.acl-long)
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| Challenge: | Current text simplification research mostly focuses on English and on sentencelevel simplification. |
| Approach: | They propose to use a dataset of parallel, professionally written and manually aligned simplifications in plain German "plain DE" and "Einfache Sprache" they build a web harvester and experiment with automatic alignment methods to facilitate integration of non-aligned and to be-published parallel documents. |
| Outcome: | The proposed dataset of parallel, professionally written and manually aligned simplifications in plain German is extended to 750 document pairs and 3.5k sentence pairs. |
MCTS: A Multi-Reference Chinese Text Simplification Dataset (2024.lrec-main)
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Ruining Chong, Luming Lu, Liner Yang, Jinran Nie, Zhenghao Liu, Shuo Wang, Shuhan Zhou, Yaoxin Li, Erhong Yang
| Challenge: | Existing studies on text simplification systems have focused on unsupervised methods due to the limited evaluation data in language and domain. |
| Approach: | They propose a Chinese text simplification dataset that provides a detailed analysis and an annotation process. |
| Outcome: | The proposed dataset evaluates the performance of unsupervised methods and advanced large language models. |
Right at My Level: A Unified Multilingual Framework for Proficiency-Aware Text Simplification (2026.acl-long)
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| Challenge: | Existing large language model-based readability control methods rely on pre-labeled sentence corpora and primarily target English. |
| Approach: | They propose a framework for adaptive multilingual text simplification without parallel corpora supervision that integrates three reward modules: vocabulary coverage, semantic preservation, and coherence. |
| Outcome: | The proposed framework achieves higher lexical coverage at target proficiency levels while maintaining original meaning and fluency compared to stronger LLMs. |
Simplify-Pro: A Two-level and Progressive LLM-based Framework for Auto Long Text Simplification (2026.findings-acl)
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| Challenge: | Existing studies have focused on lexical- and sentence-level simplification, leaving long text simplification comparatively unexplored . |
| Approach: | They propose a two-level and progressive LLM-based framework that establishes an effective paradigm for automatic long text simplification under diverse test scenarios. |
| Outcome: | The proposed framework outperforms advanced and proprietary LLMs in in-domain and out-of-domain simplification tasks and matches or outperformed existing LLM frameworks. |
REFeREE: A REference-FREE Model-Based Metric for Text Simplification (2024.lrec-main)
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| Challenge: | Existing methods for text simplification lack a universal standard of quality and require a small number of human annotations. |
| Approach: | They propose to introduce a reference-free model-based metric with a 3-stage curriculum that can be applied to any quality standard with fewer annotations. |
| Outcome: | The proposed metric outperforms existing reference-based metrics in predicting ratings while requiring no reference simplifications at inference time. |