nglaura/scielo-summarization

LoRaLay: A Multilingual and Multimodal Dataset for Long Range and Layout-Aware Summarization

A collaboration between reciTAL, MLIA (ISIR, Sorbonne Université), Meta AI, and Università di Trento

SciELO dataset for summarization

SciELO is a dataset for summarization of research papers written in Spanish and Portuguese, for which layout information is provided.

Data Fields

• article_id: article id
• article_words: sequence of words constituting the body of the article
• article_bboxes: sequence of corresponding word bounding boxes
• norm_article_bboxes: sequence of corresponding normalized word bounding boxes
• abstract: a string containing the abstract of the article
• article_pdf_url: URL of the article's PDF

Data Splits

This dataset has 3 splits: train, validation, and test.

Dataset Split	Number of Instances (ES/PT)
Train	20,853 / 19,407
Validation	1,158 / 1,078
Test	1,159 / 1,078

Citation

@article{nguyen2023loralay,
  title={LoRaLay: A Multilingual and Multimodal Dataset for Long Range and Layout-Aware Summarization},
  author={Nguyen, Laura and Scialom, Thomas and Piwowarski, Benjamin and Staiano, Jacopo},
  journal={arXiv preprint arXiv:2301.11312},
  year={2023}
}