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 classification is an area of research which has been studied over theyears in Natural Language Processing (NLP). Adapting NLP to multiple domainshas introduced many new challenges for text classification and one of them islong document classification. While state-of-the-art transformer models provideexcellent results in text classification, most of them have limitations in themaximum sequence length of the input sequence. The majority of the transformermodels are limited to 512 tokens, and therefore, they struggle with longdocument classification problems. In this research, we explore on employingModel Fusing for long document classification while comparing the results withwell-known BERT and Longformer architectures.

Can Model Fusing Help Transformers in Long Document Classification? An Empirical Study