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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

With the prevalence of video sharing, there are increasing demands forautomatic video digestion such as highlight detection. Recently, platforms withcrowdsourced time-sync video comments have emerged worldwide, providing a goodopportunity for highlight detection. However, this task is non-trivial: (1)time-sync comments often lag behind their corresponding shot; (2) time-synccomments are semantically sparse and noisy; (3) to determine which shots arehighlights is highly subjective. The present paper aims to tackle thesechallenges by proposing a framework that (1) uses concept-mapped lexical-chainsfor lag calibration; (2) models video highlights based on comment intensity andcombination of emotion and concept concentration of each shot; (3) summarizeeach detected highlight using improved SumBasic with emotion and conceptmapping. Experiments on large real-world datasets show that our highlightdetection method and summarization method both outperform other benchmarks withconsiderable margins.

Video Highlights Detection and Summarization with Lag-Calibration based on Concept-Emotion Mapping of Crowd-sourced Time-Sync Comments