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 351–400 of 468 papers

TitleStatusHype
SimpleNets: Quality Estimation with Resource-Light Neural Networks—0
Learning Paraphrasing for Multiword Expressions—0
Exploring Stylistic Variation with Age and Income on Twitter—0
Text Simplification as Tree Labeling—0
Vision and Feature Norms: Improving automatic feature norm learning through cross-modal maps—0
AmritaCEN at SemEval-2016 Task 11: Complex Word Identification using Word Embedding—0
MAZA at SemEval-2016 Task 11: Detecting Lexical Complexity Using a Decision Stump Meta-Classifier—0
UWB at SemEval-2016 Task 11: Exploring Features for Complex Word Identification—0
Melbourne at SemEval 2016 Task 11: Classifying Type-level Word Complexity using Random Forests with Corpus and Word List Features—0
SemEval 2016 Task 11: Complex Word Identification—0
LTG at SemEval-2016 Task 11: Complex Word Identification with Classifier Ensembles—0
Pomona at SemEval-2016 Task 11: Predicting Word Complexity Based on Corpus Frequency—0
MacSaar at SemEval-2016 Task 11: Zipfian and Character Features for ComplexWord Identification—0
HMC at SemEval-2016 Task 11: Identifying Complex Words Using Depth-limited Decision Trees—0
CoastalCPH at SemEval-2016 Task 11: The importance of designing your Neural Networks right—0
Evaluating the Readability of Text Simplification Output for Readers with Cognitive Disabilities—0
A Corpus of Text Data and Gaze Fixations from Autistic and Non-Autistic Adults—0
Readability-based Sentence Ranking for Evaluating Text SimplificationCode0
Optimizing Statistical Machine Translation for Text SimplificationCode0
Can Text Simplification Help Machine Translation?—0
Easy-read Documents as a Gold Standard for Evaluation of Text Simplification Output—0
Automatic Text Simplification for Spanish: Comparative Evaluation of Various Simplification Strategies—0
Readability Assessment of Translated Texts—0
Six Good Predictors of Autistic Text Comprehension—0
Input Seed Features for Guiding the Generation Process: A Statistical Approach for Spanish—0
Translating from Original to Simplified Sentences using Moses: When does it Actually Work?—0
Detecting Content-Heavy Sentences: A Cross-Language Case Study—0
Reading metrics for estimating task efficiency with MT output—0
Unsupervised Sentence Simplification Using Deep SemanticsCode0
Classifier-Based Text Simplification for Improved Machine Translation—0
A Deeper Exploration of the Standard PB-SMT Approach to Text Simplification and its Evaluation—0
Design and Annotation of the First Italian Corpus for Text Simplification—0
Reliable Lexical Simplification for Non-Native Speakers—0
Using Word Semantics To Assist English as a Second Language Learners—0
Data-driven sentence generation with non-isomorphic trees—0
CASSA: A Context-Aware Synonym Simplification Algorithm—0
A Tool for Automatic Simplification of Swedish Texts—0
Aligning Sentences from Standard Wikipedia to Simple Wikipedia—0
From distributional semantics to feature norms: grounding semantic models in human perceptual data—0
Problems in Current Text Simplification Research: New Data Can Help—0
A Keyword-based Monolingual Sentence Aligner in Text Simplification—0
An analysis of a French as a Foreign Language Corpus for Readability Assessment—0
Deep-Syntactic Parsing—0
SemEval-2014 Task 3: Cross-Level Semantic Similarity—0
Making historical texts accessible to everybody—0
Unsupervised Method for the Acquisition of General Language Paraphrases for Medical Compounds—0
Making Biographical Data in Wikipedia Readable: A Pattern-based Multilingual Approach—0
Simple or Complex? Assessing the readability of Basque Texts—0
Lexico-syntactic text simplification and compression with typed dependencies—0
The Fewer, the Better? A Contrastive Study about Ways to Simplify—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