# The SAMER Arabic Text Simplification Corpus

arXiv: https://arxiv.org/abs/2404.18615 · HTML: https://mlsanity.com/paper/2404.18615/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 90 references that are in the map.
- First posted on arXiv 29 Apr 2024.

## Builds on (most cited first, 18 of 18)

- [Sentence Simplification with Deep Reinforcement Learning](https://mlsanity.com/paper/1703.10931/) (2017)
- [Controllable Sentence Simplification](https://mlsanity.com/paper/1910.02677/) (2019)
- [Controllable Text Simplification with Explicit Paraphrasing](https://mlsanity.com/paper/2010.11004/) (2020)
- [Unsupervised Neural Text Simplification](https://mlsanity.com/paper/1810.07931/) (2018)
- [Integrating Transformer and Paraphrase Rules for Sentence Simplification](https://mlsanity.com/paper/1810.11193/) (2018)
- [Text Simplification by Tagging](https://mlsanity.com/paper/2103.05070/) (2021)
- [Dynamic Multi-Level Multi-Task Learning for Sentence Simplification](https://mlsanity.com/paper/1806.07304/) (2018)
- [A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification](https://mlsanity.com/paper/1810.05754/) (2018)
- [Simple and Effective Text Simplification Using Semantic and Neural Methods](https://mlsanity.com/paper/1810.05104/) (2018)
- [A Corpus for Automatic Readability Assessment and Text Simplification of German](https://mlsanity.com/paper/1909.09067/) (2019)
- [Klexikon: A German Dataset for Joint Summarization and Simplification](https://mlsanity.com/paper/2201.07198/) (2022)
- [Morphosyntactic Tagging with Pre-trained Language Models for Arabic and its Dialects](https://mlsanity.com/paper/2110.06852/) (2021)
- [LDC Arabic Treebanks and Associated Corpora: Data Divisions Manual](https://mlsanity.com/paper/1309.5652/) (2013)
- [Sentence Simplification with Memory-Augmented Neural Networks](https://mlsanity.com/paper/1804.07445/) (2018)
- [Camelira: An Arabic Multi-Dialect Morphological Disambiguator](https://mlsanity.com/paper/2211.16807/) (2022)
- [Arabic Word-level Readability Visualization for Assisted Text Simplification](https://mlsanity.com/paper/2210.10672/) (2022)
- [Towards Arabic Sentence Simplification via Classification and Generative Approaches](https://mlsanity.com/paper/2204.09292/) (2022)
- [Controllable Lexical Simplification for English](https://mlsanity.com/paper/2302.02900/) (2023)

## Built on by (most cited first, 9 of 9)

- [A Large and Balanced Corpus for Fine-grained Arabic Readability Assessment](https://mlsanity.com/paper/2502.13520/) (2025)
- [Opportunities and Challenges of LLMs in Education: An NLP Perspective](https://mlsanity.com/paper/2507.22753/) (2025)
- [Gazelle: An Instruction Dataset for Arabic Writing Assistance](https://mlsanity.com/paper/2410.18163/) (2024)
- [Guidelines for Fine-grained Sentence-level Arabic Readability Annotation](https://mlsanity.com/paper/2410.08674/) (2024)
- [ArabJobs: A Multinational Corpus of Arabic Job Ads](https://mlsanity.com/paper/2509.22589/) (2025)
- [Strategies for Arabic Readability Modeling](https://mlsanity.com/paper/2407.03032/) (2024)
- [!MSA at BAREC Shared Task 2025: Ensembling Arabic Transformers for Readability Assessment](https://mlsanity.com/paper/2509.10040/) (2025)
- [mucAI at BAREC Shared Task 2025: Towards Uncertainty Aware Arabic Readability Assessment](https://mlsanity.com/paper/2509.15485/) (2025)
- [Lexicon-Enriched Graph Modeling for Arabic Document Readability Prediction](https://mlsanity.com/paper/2509.22870/) (2025)

## Abstract (arXiv)

We present the SAMER Corpus, the first manually annotated Arabic parallel corpus for text simplification targeting school-aged learners. Our corpus comprises texts of 159K words selected from 15 publicly available Arabic fiction novels most of which were published between 1865 and 1955. Our corpus includes readability level annotations at both the document and word levels, as well as two simplified parallel versions for each text targeting learners at two different readability levels. We describe the corpus selection process, and outline the guidelines we followed to create the annotations and ensure their quality. Our corpus is publicly available to support and encourage research on Arabic text simplification, Arabic automatic readability assessment, and the development of Arabic pedagogical language technologies.