LoRaLay: A Multilingual and Multimodal Dataset for Long Range and Layout-Aware Summarization - Archive ouverte HAL Access content directly
Conference Papers Year : 2023

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

Laura Nguyen
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Thomas Scialom
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Jacopo Staiano
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Abstract

Text Summarization is a popular task and an active area of research for the Natural Language Processing community. It requires accounting for long input texts, a characteristic which poses computational challenges for neural models. Moreover, real-world documents come in a variety of complex, visually-rich, layouts. This information is of great relevance, whether to highlight salient content or to encode long-range interactions between textual passages. Yet, all publicly available summarization datasets only provide plain text content. To facilitate research on how to exploit visual/layout information to better capture longrange dependencies in summarization models, we present LoRaLay, a collection of datasets for long-range summarization with accompanying visual/layout information. We extend existing and popular English datasets (arXiv and PubMed) with visual/layout information and propose four novel datasets-consistently built from scholar resources-covering French, Spanish, Portuguese, and Korean languages. Further, we propose new baselines merging layout-aware and long-range models-two orthogonal approaches-and obtain state-of-theart results, showing the importance of combining both lines of research.
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Dates and versions

hal-03992015 , version 1 (16-02-2023)

Identifiers

  • HAL Id : hal-03992015 , version 1

Cite

Laura Nguyen, Thomas Scialom, Benjamin Piwowarski, Jacopo Staiano. LoRaLay: A Multilingual and Multimodal Dataset for Long Range and Layout-Aware Summarization. The 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2023), May 2023, Dubrovnik, Croatia. ⟨hal-03992015⟩
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