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

• URL: http://arxiv.org/abs/2301.11312v1
• Date: Thu, 26 Jan 2023 18:50:54 GMT
• Title: LoRaLay: A Multilingual and Multimodal Dataset for Long Range and Layout-Aware Summarization
• Authors: Laura Nguyen, Thomas Scialom, Benjamin Piwowarski, Jacopo Staiano
• Abstract summary: Text Summarization is a popular task and an active area of research for the Natural Language Processing community. All publicly available summarization datasets only provide plain text content. We present LoRaLay, a collection of datasets for long-range summarization with accompanying visual/Lay information.
• Score: 19.301567079372436
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text Summarization is a popular task and an active area of research for the Natural Language Processing community. By definition, it requires to account 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 long-range 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 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-the-art results, showing the importance of combining both lines of research.

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