Presentation on "Metadata Driven TV Content re-purposing and re-publication" by Lyndon Nixon, MODUL Technology. Presented at the EBU MDN Workshop 2020

1. Metadata Driven TV
Content Re-Purposing
and Re-Publication
Lyndon Nixon, MODUL Technology
EBU MDN 2020, 9/6/2020

2. 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
and targeting.

3. ReTV develops aTrans-Vector Platform
(TVP) to analyse content across all channels
and “publish to all media vectors with the
effort of one”

4. End UserTools Enabled ByTheTVP

5. Topics Compass: what are the trending topics to
publish about?

6. Topics Compass metadata: keywords and entities
International news articles
Online pages aboutTV content on
broadcasters Websites
Social media accounts aboutTV
content
TV Program information from EPG
data
Related content (mentioning aTV
program) from social media

7. Success metrics: frequency, impact, sentiment and
WYSDOM

8. Prediction: what is the best topic to choose on a future
date?
Our events and anniversaries API highlights
important events and anniversaries on a specific
date.
TheTopics Compass can identify time references
in documents and aggregate those documents
that refer to a specific date

9. Prediction: what is the best date to choose for a future
topic?

10. Topics Compass 5min demo

11. Content Wizard: what content do I publish to get the
audience‘s attention?

12. Content Wizard: re-purposing content for the channel

13. Video summarization
13
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
reconstructed video.

14. Content Wizard: recommending when to schedule the
publication

15. Content Wizard: 5min demo

16. 4u2: how viewers can access new personalised services
with your content

17. 4u2: how viewers can access new personalised services
with your content

18. 4u2: 5min demo

19. The ReTV Stakeholder Forum is your
opportunity to engage with us, be first
to get updates and have the opportunity
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
(lyndon.nixon@modul.ac.at)
Sign up for the ReTV Newsletter and get
an update every few months from us!

20. @ReTV_EU Facebook: ReTVeuwww.ReTV-Project.eu Instagram: retv_project
Dr. Lyndon Nixon
ReTV Project Coordinator
info@retv-project.eu
@ReTV_EU @ReTVeu
ReTV Project retv_project