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

Reading comprehension of legal text can be a particularly challenging taskdue to the length and complexity of legal clauses and a shortage ofexpert-annotated datasets. To address this challenge, we introduce the MergerAgreement Understanding Dataset (MAUD), an expert-annotated readingcomprehension dataset based on the American Bar Association's 2021 PublicTarget Deal Points Study, with over 39,000 examples and over 47,000 totalannotations. Our fine-tuned Transformer baselines show promising results, withmodels performing well above random on most questions. However, on a largesubset of questions, there is still room for significant improvement. As theonly expert-annotated merger agreement dataset, MAUD is valuable as a benchmarkfor both the legal profession and the NLP community.

MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding