Summary of An Open Multilingual System For Scoring Readability Of Wikipedia, by Mykola Trokhymovych et al.

An Open Multilingual System for Scoring Readability of Wikipedia

by Mykola Trokhymovych, Indira Sen, Martin Gerlach

First submitted to arxiv on: 3 Jun 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary The paper presents a multilingual model to assess the readability of Wikipedia articles across 300+ languages. The model is trained and evaluated using a novel dataset spanning 14 languages, comprising matched articles from Wikipedia and simplified encyclopedias for children. The results show the model’s effectiveness in a zero-shot scenario, achieving over 80% ranking accuracy across languages and outperforming previous benchmarks. This achievement highlights the potential for large-scale readability assessment without requiring ground-truth data fine-tuning.
Low	GrooveSquid.com (original content)	Low Difficulty Summary Wikipedia is the biggest platform for free knowledge, with millions of articles to read. But many people can’t understand the text because it’s too hard. Previous studies only looked at English Wikipedia and didn’t help with other languages. To fix this, researchers developed a model that scores the readability of Wikipedia articles in many languages. They created a new dataset by matching articles from Wikipedia to simpler texts for children. The results show their model works well without needing special training data. This breakthrough helps make it easier for people to read and understand Wikipedia’s vast content.