Text Simplification
117 papers with code • 11 benchmarks • 20 datasets
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.
Benchmarks
These leaderboards are used to track progress in Text Simplification
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Libraries
Use these libraries to find Text Simplification models and implementationsDatasets
Newsela
WikiLarge
ASSET
CEFR-SP
Most implemented papers
FASTSUBS: An Efficient and Exact Procedure for Finding the Most Likely Lexical Substitutes Based on an N-gram Language Model
Lexical substitutes have found use in areas such as paraphrasing, text simplification, machine translation, word sense disambiguation, and part of speech induction.
Unsupervised Sentence Simplification Using Deep Semantics
We present a novel approach to sentence simplification which departs from previous work in two main ways.
Optimizing Statistical Machine Translation for Text Simplification
Most recent sentence simplification systems use basic machine translation models to learn lexical and syntactic paraphrases from a manually simplified parallel corpus.
Readability-based Sentence Ranking for Evaluating Text Simplification
We propose a new method for evaluating the readability of simplified sentences through pair-wise ranking.
Sentence Simplification with Deep Reinforcement Learning
XingxingZhang/dress
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EMNLP 2017
Sentence simplification aims to make sentences easier to read and understand.
Exploring Neural Text Simplification Models
senisioi/NeuralTextSimplification
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ACL 2017
Unlike the previously proposed automated TS systems, our neural text simplification (NTS) systems are able to simultaneously perform lexical simplification and content reduction.
A Semantic Relevance Based Neural Network for Text Summarization and Text Simplification
shumingma/SRB
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In this work, our goal is to improve semantic relevance between source texts and simplified texts for text summarization and text simplification.
Learning How to Simplify From Explicit Labeling of Complex-Simplified Text Pairs
Current research in text simplification has been hampered by two central problems: (i) the small amount of high-quality parallel simplification data available, and (ii) the lack of explicit annotations of simplification operations, such as deletions or substitutions, on existing data.
Query and Output: Generating Words by Querying Distributed Word Representations for Paraphrase Generation
The existing sequence-to-sequence model tends to memorize the words and the patterns in the training dataset instead of learning the meaning of the words.
Inherent Biases in Reference based Evaluation for Grammatical Error Correction and Text Simplification
The prevalent use of too few references for evaluating text-to-text generation is known to bias estimates of their quality ({\it low coverage bias} or LCB).
