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
Latest papers
Simpler becomes Harder: Do LLMs Exhibit a Coherent Behavior on Simplified Corpora?
Text simplification seeks to improve readability while retaining the original content and meaning.
EASSE-DE: Easier Automatic Sentence Simplification Evaluation for German
In this work, we propose EASSE-multi, a framework for easier automatic sentence evaluation for languages other than English.
REFeREE: A REference-FREE Model-Based Metric for Text Simplification
Text simplification lacks a universal standard of quality, and annotated reference simplifications are scarce and costly.
mEdIT: Multilingual Text Editing via Instruction Tuning
We introduce mEdIT, a multi-lingual extension to CoEdIT -- the recent state-of-the-art text editing models for writing assistance.
German Text Simplification: Finetuning Large Language Models with Semi-Synthetic Data
This study pioneers the use of synthetically generated data for training generative models in document-level text simplification of German texts.
MedTSS: transforming abstractive summarization of scientific articles with linguistic analysis and concept reinforcement
This research addresses the limitations of pretrained models (PTMs) in generating accurate and comprehensive abstractive summaries for scientific articles, with a specific focus on the challenges posed by medical research.
InfoLossQA: Characterizing and Recovering Information Loss in Text Simplification
Text simplification aims to make technical texts more accessible to laypeople but often results in deletion of information and vagueness.
Exploring Automatic Text Simplification of German Narrative Documents
In this paper, we apply transformer-based Natural Language Generation (NLG) techniques to the problem of text simplification.
Do Text Simplification Systems Preserve Meaning? A Human Evaluation via Reading Comprehension
With this framework, we conduct a thorough human evaluation of texts by humans and by nine automatic systems.
Ascle: A Python Natural Language Processing Toolkit for Medical Text Generation
This study introduces Ascle, a pioneering natural language processing (NLP) toolkit designed for medical text generation.
