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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 201–250 of 468 papers

TitleStatusHype
Splitting Complex English Sentences—0
Statistical Machine Translation with Readability Constraints—0
Subjective Assessment of Text Complexity: A Dataset for German Language—0
Summarising News Stories for Children—0
Eye Tracking Based Cognitive Evaluation of Automatic Readability Assessment Measures—0
Syntactic Sentence Simplification for French—0
Text Simplification as Tree Labeling—0
Text Simplification as Tree Transduction—0
Text Simplification from Professionally Produced Corpora—0
Text Simplification of College Admissions Instructions: A Professionally Simplified and Verified Corpus—0
Text Simplification of Scientific Texts for Non-Expert Readers—0
Text Simplification to Help Individuals with Low Vision Read More Fluently—0
Text Simplification Tools for Spanish—0
Text simplification using synchronous dependency grammars: Generalising automatically harvested rules—0
Text Simplification with Reinforcement Learning Using Supervised Rewards on Grammaticality, Meaning Preservation, and Simplicity—0
The Common Readability Formula & Five Adjusted Readability Formulas for Text Simplification, Medical Documents and Other General Uses—0
The C-Score -- Proposing a Reading Comprehension Metrics as a Common Evaluation Measure for Text Simplification—0
The descriptive approaches of the verb in Linguistics, Terminology and NLP (Les mod\`eles de description du verbe dans les travaux de Linguistique, Terminologie et TAL) [in French]—0
The Fewer Splits are Better: Deconstructing Readability in Sentence Splitting—0
The Fewer, the Better? A Contrastive Study about Ways to Simplify—0
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics—0
The Impact of Rule-Based Text Generation on the Quality of Abstractive Summaries—0
The Interface Between Readability and Automatic Text Simplification—0
The Lay Person's Guide to Biomedicine: Orchestrating Large Language Models—0
The Promise and Challenges of Using LLMs to Accelerate the Screening Process of Systematic Reviews—0
The SAMER Arabic Text Simplification Corpus—0
The Swedish Simplification Toolkit: – Designed with Target Audiences in Mind—0
Towards Automatic Lexical Simplification in Spanish: An Empirical Study—0
Towards more patient friendly clinical notes through language models and ontologies—0
Towards Readability-Controlled Machine Translation of COVID-19 Texts—0
Towards Robust Abstractive Multi-Document Summarization: A Caseframe Analysis of Centrality and Domain—0
Traditional Readability Formulas Compared for English—0
Translating from Original to Simplified Sentences using Moses: When does it Actually Work?—0
Understanding the Lexical Simplification Needs of Non-Native Speakers of English—0
Unsupervised Method for the Acquisition of General Language Paraphrases for Medical Compounds—0
Unsupervised Resolution of Acronyms and Abbreviations in Nursing Notes Using Document-Level Context Models—0
Unsupervised Sentence Simplification via Dependency Parsing—0
Unsupervised Simplification of Legal Texts—0
UOW-SHEF: SimpLex -- Lexical Simplicity Ranking based on Contextual and Psycholinguistic Features—0
Uses of Monolingual In-Domain Corpora for Cross-Domain Adaptation with Hybrid MT Approaches—0
Using Gaze to Predict Text Readability—0
Using Word Semantics To Assist English as a Second Language Learners—0
Utilisation d'une base de connaissances de sp\'ecialit\'e et de sens commun pour la simplification de comptes-rendus radiologiques (Radiological text simplification using a general knowledge base)—0
UWB at SemEval-2016 Task 11: Exploring Features for Complex Word Identification—0
Vicinity-Driven Paragraph and Sentence Alignment for Comparable Corpora—0
Vision and Feature Norms: Improving automatic feature norm learning through cross-modal maps—0
VTechAGP: An Academic-to-General-Audience Text Paraphrase Dataset and Benchmark Models—0
What Makes a Concept Complex? Measuring Conceptual Complexity as a Precursor for Text Simplification—0
DisSim: A Discourse-Aware Syntactic Text Simplification Framework for English and German—0
DisSim-FinBERT: Text Simplification for Core Message Extraction in Complex Financial Texts—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