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

Spreadsheets are widely used for table manipulation and presentation.Stylistic formatting of these tables is an important property for bothpresentation and analysis. As a result, popular spreadsheet software, such asExcel, supports automatically formatting tables based on rules. Unfortunately,writing such formatting rules can be challenging for users as it requiresknowledge of the underlying rule language and data logic. We present CORNET, asystem that tackles the novel problem of automatically learning such formattingrules from user examples in the form of formatted cells. CORNET takesinspiration from advances in inductive programming and combines symbolic ruleenumeration with a neural ranker to learn conditional formatting rules. Tomotivate and evaluate our approach, we extracted tables with over 450K uniqueformatting rules from a corpus of over 1.8M real worksheets. Since we are thefirst to introduce conditional formatting, we compare CORNET to a wide range ofsymbolic and neural baselines adapted from related domains. Our results showthat CORNET accurately learns rules across varying evaluation setups.Additionally, we show that CORNET finds shorter rules than those that a userhas written and discovers rules in spreadsheets that users have manuallyformatted.

CORNET: Learning Table Formatting Rules By Example