English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Text Summarization is a popular task and an active area of research for theNatural Language Processing community. By definition, it requires to accountfor long input texts, a characteristic which poses computational challenges forneural models. Moreover, real-world documents come in a variety of complex,visually-rich, layouts. This information is of great relevance, whether tohighlight salient content or to encode long-range interactions between textualpassages. Yet, all publicly available summarization datasets only provide plaintext content. To facilitate research on how to exploit visual/layoutinformation to better capture long-range dependencies in summarization models,we present LoRaLay, a collection of datasets for long-range summarization withaccompanying visual/layout information. We extend existing and popular Englishdatasets (arXiv and PubMed) with layout information and propose four noveldatasets -- consistently built from scholar resources -- covering French,Spanish, Portuguese, and Korean languages. Further, we propose new baselinesmerging layout-aware and long-range models -- two orthogonal approaches -- andobtain state-of-the-art results, showing the importance of combining both linesof research.

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