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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Keyphrase extraction from documents is useful to a variety of applicationssuch as information retrieval and document summarization. This paper presentsan end-to-end method called DivGraphPointer for extracting a set of diversifiedkeyphrases from a document. DivGraphPointer combines the advantages oftraditional graph-based ranking methods and recent neural network-basedapproaches. Specifically, given a document, a word graph is constructed fromthe document based on word proximity and is encoded with graph convolutionalnetworks, which effectively capture document-level word salience by modelinglong-range dependency between words in the document and aggregating multipleappearances of identical words into one node. Furthermore, we propose adiversified point network to generate a set of diverse keyphrases out of theword graph in the decoding process. Experimental results on five benchmark datasets show that our proposed method significantly outperforms the existingstate-of-the-art approaches.

DivGraphPointer: A Graph Pointer Network for Extracting Diverse Keyphrases