Text Simplification
63 papers with code • 5 benchmarks • 11 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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Text Simplification models and implementationsDatasets
Newsela
WikiLarge
ASSET
Most implemented papers
Complexity-Weighted Loss and Diverse Reranking for Sentence Simplification
Sentence simplification is the task of rewriting texts so they are easier to understand.
Controllable Sentence Simplification
facebookresearch/access
•
•
LREC 2020
Text simplification aims at making a text easier to read and understand by simplifying grammar and structure while keeping the underlying information identical.
Felix: Flexible Text Editing Through Tagging and Insertion
google-research/google-research
•
•
Findings of the Association for Computational Linguistics 2020
We achieve this by decomposing the text-editing task into two sub-tasks: tagging to decide on the subset of input tokens and their order in the output text and insertion to in-fill the missing tokens in the output not present in the input.
HTSS: A Novel Hybrid Text Summarisation and Simplification Architecture
farooqzaman1/HTSS
•
•
Information Processing and Management 2020
Our results show that our proposed HTSS model outperforms neural text simplification (NTS) on SARI score and abstractive text summarisation (ATS) on the ROUGE score.
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
•
•
EMNLP 2017
Sentence simplification aims to make sentences easier to read and understand.
Exploring Neural Text Simplification Models
senisioi/NeuralTextSimplification
•
•
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.
