metadata
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: inst_no
dtype: int64
- name: system
dtype: string
splits:
- name: train
num_bytes: 55286755
num_examples: 20000
- name: validation
num_bytes: 2408874
num_examples: 1000
- name: test
num_bytes: 10404070
num_examples: 5000
download_size: 33379631
dataset_size: 68099699
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
Original Dataset
@inproceedings{scialom-etal-2020-mlsum,
title = "{MLSUM}: The Multilingual Summarization Corpus",
author = "Scialom, Thomas and
Dray, Paul-Alexis and
Lamprier, Sylvain and
Piwowarski, Benjamin and
Staiano, Jacopo",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.647",
doi = "10.18653/v1/2020.emnlp-main.647",
pages = "8051--8067",
abstract = "We present MLSUM, the first large-scale MultiLingual SUMmarization dataset. Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages {--} namely, French, German, Spanish, Russian, Turkish. Together with English news articles from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community. We report cross-lingual comparative analyses based on state-of-the-art systems. These highlight existing biases which motivate the use of a multi-lingual dataset.",
}