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

Sensemaking in unfamiliar domains can be challenging, demanding considerableuser effort to compare different options with respect to various criteria.Prior research and our formative study found that people would benefit fromreading an overview of an information space upfront, including the criteriaothers previously found useful. However, existing sensemaking tools strugglewith the "cold-start" problem -- not only requiring significant input fromprevious users to generate and share these overviews, but also that suchoverviews may turn out to be biased and incomplete. In this work, we introducea novel system, Selenite, which leverages LLMs as reasoning machines andknowledge retrievers to automatically produce a comprehensive overview ofoptions and criteria to jumpstart users' sensemaking processes. Subsequently,Selenite also adapts as people use it, helping users find, read, and navigateunfamiliar information in a systematic yet personalized manner. Through threestudies, we found that Selenite produced accurate and high-quality overviewsreliably, significantly accelerated users' information processing, andeffectively improved their overall comprehension and sensemaking experience.

Selenite: Scaffolding Online Sensemaking with Comprehensive Overviews Elicited from Large Language Models