20min-XD: A Comparable Corpus of Swiss News Articles

Michelle Wastl, Jannis Vamvas, Selena Calleri, Rico Sennrich
4/30/2025
cs.CL

No official code found. Best implementation: ZurichNLP/20min-XD (0 stars, MIT, last commit 2026-01-19).

Abstract

We present 20min-XD (20 Minuten cross-lingual document-level), a French-German, document-level comparable corpus of news articles, sourced from the Swiss online news outlet 20 Minuten/20 minutes. Our dataset comprises around 15,000 article pairs spanning 2015 to 2024, automatically aligned based on semantic similarity. We detail the data collection process and alignment methodology. Furthermore, we provide a qualitative and quantitative analysis of the corpus. The resulting dataset exhibits a broad spectrum of cross-lingual similarity, ranging from near-translations to loosely related articles, making it valuable for various NLP applications and broad linguistically motivated studies. We publicly release the dataset in document- and sentence-aligned versions and code for the described experiments.

View on arXivView PDF

Code Implementations(1)

ZurichNLP/20min-XDCommunity100%
MIT

Repository containing all code for the 20min-XD: A Comparable Corpus of Swiss News Articles paper.

00PythonApr 23, 20259 months agoMIT

Similar Papers

A Multilingual Similarity Dataset for News Article Frame

81% match1 repo42024

Building and Aligning Comparable Corpora

81% match2025

A Massive Scale Semantic Similarity Dataset of Historical English

78% match2026

A diverse Multilingual News Headlines Dataset from around the World

77% match1 repo62024

SwissGov-RSD: A Human-annotated, Cross-lingual Benchmark for Token-level Recognition of Semantic Differences Between Related Documents

76% match2025

CrossNews-UA: A Cross-lingual News Semantic Similarity Benchmark for Ukrainian, Polish, Russian, and English

76% match1 repo2025

Cite this paper

@article{wastl2025minxd,
  title  = {20min-XD: A Comparable Corpus of Swiss News Articles},
  author = {Michelle Wastl and Jannis Vamvas and Selena Calleri and Rico Sennrich},
  year   = {2025},
  eprint = {2504.21677},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2504.21677v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2504.21677.svg)](https://codeofpaper.com/paper/2504.21677)

Discussion