SOTAVerified

Text Simplification

Text Simplification is the task of reducing the complexity of the vocabulary and sentence structure of text while retaining its original meaning, with the goal of improving readability and understanding. Simplification has a variety of important societal applications, for example increasing accessibility for those with cognitive disabilities such as aphasia, dyslexia, and autism, or for non-native speakers and children with reading difficulties.

Source: Multilingual Unsupervised Sentence Simplification

Papers

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Showing 251–300 of 468 papers

TitleStatusHype
Divide-and-Conquer Text Simplification by Scalable Data Enhancement—0
Domain Adaptation in Multilingual and Multi-Domain Monolingual Settings for Complex Word Identification—0
DSim, a Danish Parallel Corpus for Text Simplification—0
Dynamic Multi-Level Multi-Task Learning for Sentence Simplification—0
Easy-read Documents as a Gold Standard for Evaluation of Text Simplification Output—0
Elaborative Simplification as Implicit Questions Under Discussion—0
Enabling text readability awareness during the micro planning phase of NLG applications—0
Evaluating Document Simplification: On the Importance of Separately Assessing Simplicity and Meaning Preservation—0
Evaluating GenAI for Simplifying Texts for Education: Improving Accuracy and Consistency for Enhanced Readability—0
Evaluating the Readability of Text Simplification Output for Readers with Cognitive Disabilities—0
Evaluation of Automatic Text Simplification: Where are we now, where should we go from here—0
Evaluation Under Imperfect Benchmarks and Ratings: A Case Study in Text Simplification—0
Event-Centered Simplification of News Stories—0
Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymen—0
Explicit Retrofitting of Distributional Word Vectors—0
Exploring Large Language Models to generate Easy to Read content—0
Exploring Stylistic Variation with Age and Income on Twitter—0
Exploring the effects of Sentence Simplification on Hindi to English Machine Translation System—0
Exploring Verb Frames for Sentence Simplification in Hindi—0
Few-shot Controllable Style Transfer for Low-Resource Multilingual Settings—0
Few-shot Controllable Style Transfer for Low-Resource Multilinugal Settings—0
Findings of the TSAR-2022 Shared Task on Multilingual Lexical Simplification—0
FLELex: a graded lexical resource for French foreign learners—0
Flesch-Kincaid is Not a Text Simplification Evaluation Metric—0
Framework for the Analysis of Simplified Texts Taking Discourse into Account: the Basque Causal Relations as Case Study—0
French and German Corpora for Audience-based Text Type Classification—0
French Biomedical Text Simplification: When Small and Precise Helps—0
From distributional semantics to feature norms: grounding semantic models in human perceptual data—0
From Shakespeare to Twitter: What are Language Styles all about?—0
Generating Animations from Screenplays—0
HECTOR: A Hybrid TExt SimplifiCation TOol for Raw Texts in French—0
HMC at SemEval-2016 Task 11: Identifying Complex Words Using Depth-limited Decision Trees—0
Controlling Pre-trained Language Models for Grade-Specific Text Simplification—0
Hybrid Simplification using Deep Semantics and Machine Translation—0
Hybrid text simplification using synchronous dependency grammars with hand-written and automatically harvested rules—0
Identification of Parallel Sentences in Comparable Monolingual Corpora from Different Registers—0
Identifying Abstract and Concrete Words in French to Better Address Reading Difficulties—0
Improvements to Dependency Parsing Using Automatic Simplification of Data—0
Improving Estonian Text Simplification through Pretrained Language Models and Custom Datasets—0
Improving Human Text Simplification with Sentence Fusion—0
Improving Machine Translation of English Relative Clauses with Automatic Text Simplification—0
Improving Neural Text Simplification Model with Simplified Corpora—0
Improving Text Simplification Language Modeling Using Unsimplified Text Data—0
Input Seed Features for Guiding the Generation Process: A Statistical Approach for Spanish—0
Is Character Trigram Overlapping Ratio Still the Best Similarity Measure for Aligning Sentences in a Paraphrased Corpus?—0
Is it Possible to Modify Text to a Target Readability Level? An Initial Investigation Using Zero-Shot Large Language Models—0
Joint Copying and Restricted Generation for Paraphrase—0
JUST-BLUE at SemEval-2021 Task 1: Predicting Lexical Complexity using BERT and RoBERTa Pre-trained Language Models—0
Korean L2 Vocabulary Prediction: Can a Large Annotated Corpus be Used to Train Better Models for Predicting Unknown Words?—0
Label Confidence Weighted Learning for Target-level Sentence Simplification—0
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All datasetsTurkCorpusASSETNewselaPWKP / WikiSmallDEplain-APA-docDEplain-web-docEurekaAlertDEplain-APA-sentDEplain-web-sentWikiLargeFRWiki-Auto + Turk

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GPT-175B (6 SARI-selected examples, high/low)SARI (EASSE>=0.2.1)43.46—Unverified
2MUSS (BART+ACCESS Supervised)SARI (EASSE>=0.2.1)42.53—Unverified
3Control Prefixes (BART)SARI (EASSE>=0.2.1)42.32—Unverified
4TSTSARI (EASSE>=0.2.1)41.46—Unverified
5ACCESSSARI (EASSE>=0.2.1)41.38—Unverified
6MUSS (BART+ACCESS Unsupervised)SARI (EASSE>=0.2.1)40.85—Unverified
7DMASS-DCSSSARI (EASSE>=0.2.1)40.45—Unverified
8SBMT-SARISARI (EASSE>=0.2.1)39.56—Unverified
9EditNTSSARI (EASSE>=0.2.1)38.22—Unverified
10PBMT-RSARI (EASSE>=0.2.1)38.04—Unverified
#ModelMetricClaimedVerifiedStatus
1GPT-175B (15 SARI-selected examples, random ordering)BLEU73.92—Unverified
2MUSS (BART+ACCESS Supervised)BLEU72.98—Unverified
3Control Prefixes (BART)SARI (EASSE>=0.2.1)43.58—Unverified
4TSTSARI (EASSE>=0.2.1)43.21—Unverified
5MUSS (BART+ACCESS Unsupervised)SARI (EASSE>=0.2.1)42.65—Unverified
6ACCESSSARI (EASSE>=0.2.1)40.13—Unverified
7DMASS-DCSSSARI (EASSE>=0.2.1)38.67—Unverified
8Dress-LSSARI (EASSE>=0.2.1)36.59—Unverified
9UNTS (Unsupervised)SARI (EASSE>=0.2.1)35.19—Unverified
10PBMT-RSARI (EASSE>=0.2.1)34.63—Unverified
#ModelMetricClaimedVerifiedStatus
1CRF Alignment + TransformerSARI36.6—Unverified
2Pointer + Multi-task Entailment and Paraphrase GenerationSARI33.22—Unverified
3EditNTSSARI31.41—Unverified
4S2S-Cluster-FASARI30.73—Unverified
5Edit-Unsup-TSSARI30.44—Unverified
6NSELSTM-SSARI29.58—Unverified
7NSELSTM-BSARI27.42—Unverified
8DRESSSARI27.37—Unverified
9DMASS + DCSSSARI27.28—Unverified
10DRESS-LSSARI26.63—Unverified
#ModelMetricClaimedVerifiedStatus
1NSELSTM-BBLEU53.42—Unverified
2TSTSARI44.67—Unverified
3UNSUPBLEU38.47—Unverified
4DRESS-LSBLEU36.32—Unverified
5DRESSBLEU34.53—Unverified
6EditNTSSARI32.35—Unverified
7NSELSTM-SBLEU29.72—Unverified
8Pointer + Multi-task Entailment and Paraphrase GenerationBLEU27.23—Unverified
#ModelMetricClaimedVerifiedStatus
1long-mBART (trained on DEplain-APA-doc)SARI (EASSE>=0.2.1)44.56—Unverified
2long-mBART (trained on DEplain-APA-doc & DEplain-web-doc)SARI (EASSE>=0.2.1)42.86—Unverified
3long-mBART (trained on DEplain-web-doc)SARI (EASSE>=0.2.1)35.02—Unverified
#ModelMetricClaimedVerifiedStatus
1long-mBART (trained on DEplain-APA-doc & DEplain-web-doc)SARI (EASSE>=0.2.1)49.75—Unverified
2long-mBART (trained on DEplain-web-doc)SARI (EASSE>=0.2.1)49.58—Unverified
3long-mBART (trained on DEplain-APA-doc)SARI (EASSE>=0.2.1)43.09—Unverified
#ModelMetricClaimedVerifiedStatus
1SATSSARI40.83—Unverified
2MedTSS-BART (Without Training)Rouge135.17—Unverified
3HTSSRouge121.94—Unverified
#ModelMetricClaimedVerifiedStatus
1mBART (trained on DEplain-APA-sent & DEplain-web-sent)SARI (EASSE>=0.2.1)34.9—Unverified
2mBART (trained on DEplain-APA-sent)SARI (EASSE>=0.2.1)34.82—Unverified
#ModelMetricClaimedVerifiedStatus
1mBART (trained on DEplain-APA-sent & DEplain-web-sent)SARI (EASSE>=0.2.1)34.83—Unverified
2mBART (trained on DEplain-APA-sent)SARI (EASSE>=0.2.1)30.87—Unverified
#ModelMetricClaimedVerifiedStatus
1mT5 (fine-tuned on MULTI-SIM)SARI39.23—Unverified
2LSTMSARI39.05—Unverified
#ModelMetricClaimedVerifiedStatus
1BART (TextBox 2.0)BLEU-490.81—Unverified