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

Most current extractive summarization models generate summaries by selectingsalient sentences. However, one of the problems with sentence-level extractivesummarization is that there exists a gap between the human-written gold summaryand the oracle sentence labels. In this paper, we propose to extract fact-levelsemantic units for better extractive summarization. We also introduce ahierarchical structure, which incorporates the multi-level of granularities ofthe textual information into the model. In addition, we incorporate our modelwith BERT using a hierarchical graph mask. This allows us to combine BERT'sability in natural language understanding and the structural informationwithout increasing the scale of the model. Experiments on the CNN/DaliyMaildataset show that our model achieves state-of-the-art results.

Fact-level Extractive Summarization with Hierarchical Graph Mask on BERT