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

Although Seq2Seq models for table-to-text generation have achieved remarkableprogress, modeling table representation in one dimension is inadequate. This isbecause (1) the table consists of multiple rows and columns, which means thatencoding a table should not depend only on one dimensional sequence or set ofrecords and (2) most of the tables are time series data (e.g. NBA game data,stock market data), which means that the description of the current table maybe affected by its historical data. To address aforementioned problems, notonly do we model each table cell considering other records in the same row, wealso enrich table's representation by modeling each table cell in context ofother cells in the same column or with historical (time dimension) datarespectively. In addition, we develop a table cell fusion gate to combinerepresentations from row, column and time dimension into one dense vectoraccording to the saliency of each dimension's representation. We evaluated ourmethods on ROTOWIRE, a benchmark dataset of NBA basketball games. Bothautomatic and human evaluation results demonstrate the effectiveness of ourmodel with improvement of 2.66 in BLEU over the strong baseline andoutperformance of state-of-the-art model.

Table-to-Text Generation with Effective Hierarchical Encoder on Three Dimensions (Row, Column and Time)