MLSUM: The Multilingual Summarization Corpus

By Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano. 2020

213 citations recorded by the source corpus.

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 newspapers 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.

Subfields: Artificial Intelligence, Natural Language Processing, Evaluation & Benchmarks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Natural Language Processing

In Evaluation & Benchmarks

Citations in the corpus

PaLM: Scaling Language Modeling with Pathways (2022, methodology): “The datasets, languages, and brief task descriptions are as follows: • MLSum (Scialom et al., 2020) – Summarize a news article in multiple sentences.”
Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Text (2022, methodology): “Additionally, only two of the 27 datasets were nonEnglish, despite much recent work that introduces multilingual summarization corpora (Giannakopoulos et al., 2015; Scialom et al., 2020; Ladhak et al., 2020; Hasan et al., 2021; Perez-Beltrachini and Lapata, 2021).”
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics (2021, background): “Unfortunately, nonEnglish datasets were ranked lower, with only WebNLG and MLSum among the top 15 datasets.”
LongForm: Effective Instruction Tuning with Reverse Instructions (2023, neutral): “We now present a comprehensive evaluation of our LongForm models compared to the baselines on LongForm-C test, recipe generation, long form question answering, short story generation, NLU, and ablation of LongForm-C subsets.”
GLM-130B: An Open Bilingual Pre-trained Model (2022, methodology): “, 2019) and WikiLingua (Scialom et al., 2020) from GEM generation benchmark (Gehrmann et al.”
Pre-training via Paraphrasing (2020, methodology): “We use the MLSum dataset [Scialom et al., 2020] to compare performance in several languages.”
Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy (2022, background): “‚ längst überfäll MLSum-es*(Scialometal., 2020) 100.0 1: Some benchmark tasks could be significantly affected by M EM F REE .”
AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model (2022, methodology): “We show that AlexaTM 20B performs better than or on par with PaLM 540B on XSUM (Narayan et al., 2018) and MLSum (de and es) (Scialom et al., 2020) both in one-shot in-context learning and when ﬁne-tuned.”
A Comprehensive Overview of Large Language Models (2023, unknown): “SQuAD [336], BoolQ [337], SQUADv2 [338], DROP [339], RTE [340], WebQA [341], CMRC2017 [342], CMRC2018 [343], CMRC2019 [344], COTE-BD [345], COTE-DP [345], COTE-MFW [345], MultiRC [346], Natural Questions [347], CNSE [301], DRCD [348], DuReader [349], Dureader robust [350], DuReader-QG [349], SciQ [3”
Models and Datasets for Cross-Lingual Summarisation (2022, background): “, building monolingual summarisation systems for different languages) has been gaining momentum (Chi et al., 2020b; Scialom et al., 2020).”
XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages (2021, background): “Cao et al. (2020); Scialom et al. (2020) introduced two new datasets for multilingual summarization, but both were limited to less than 10 languages.”
Automatic Construction of Evaluation Suites for Natural Language Generation Datasets (2021, background): “Text-to-text MLSum MLde/es de, es Summarize news articles [36] Wiki-Auto +TURK/ASSET WKT/A en Simplify a given sentence [37, 38] XSum XS en Summarize a news article in one sentence [39]”
Understanding Generative AI Content with Embedding Models (2024, background): “[33] text Mistral-7b [34] Detect AI generated text Identify generative model MLSUM [35] text multilingual-e5-large [36] Identify language Identify topic Detect AI translations ArXiV text Mistral-7b [34] Identify topic Detect AI generated text Identify prompt LSUN [37] images Data Filtering Network…”