Viewing of linear broadcastTV is
decreasing while time spent with digital
content on CatchupTV, on-demand OTT or
social media rises.
Broadcaster audiences are fragmented
across digital channels and digital channels
are full of competing content offers for
their limited attention.
TheTV industry is still catching up with
their online competition in the use ofWeb
technology: user tracking, personalisation
Topics Compass metadata: keywords and entities
International news articles
Online pages aboutTV content on
Social media accounts aboutTV
TV Program information from EPG
Related content (mentioning aTV
program) from social media
Prediction: what is the best topic to choose on a future
Our events and anniversaries API highlights
important events and anniversaries on a specific
TheTopics Compass can identify time references
in documents and aggregate those documents
that refer to a specific date
1. Video length restrictions (e.g. social media)
2. According to topic(s) (predicted to optimise success)
3. Guided by purpose (e.g. trailer to promote future content, highlights of past content)
The SUM-GAN model
•Idea: learn keyframe selection by minimizing the
distance between the deep feature representations of
the original video and a reconstructed version
•Problem: how to define a good distance?
•Solution: train a discriminator network (GAN)!
•Goal: train Summarizer to maximally confuse the
discriminator when distinguishing the original from the
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to test our tools and applications!
I am here all day for live demos of any of
the ReTV tools – just send me a message
in Skype (lyndonjbnixon) / e-mail
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