Text Simplification
88 papers with code • 6 benchmarks • 15 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
CEFR-SP
Most implemented papers
DiscoFuse: A Large-Scale Dataset for Discourse-Based Sentence Fusion
We author a set of rules for identifying a diverse set of discourse phenomena in raw text, and decomposing the text into two independent sentences.
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.
Control Prefixes for Parameter-Efficient Text Generation
Prefix-tuning is a powerful lightweight technique for adapting a large pre-trained language model to a downstream application.
Klexikon: A German Dataset for Joint Summarization and Simplification
Traditionally, Text Simplification is treated as a monolingual translation task where sentences between source texts and their simplified counterparts are aligned for training.
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.
