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FAIRview: Responsible Video Summarization @NYCML'18

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Presentation at the NYC Media Lab (NYCML2018). There is a growing demand for news videos online, with more consumers preferring to watch the news than read or listen to it. On the publisher side, there is a growing effort to use video summarization technology in order to create easy-to-consume previews (trailers) for different types of broadcast programs. How can we measure the quality of video summaries and their potential to misinform? This workshop will inform participants about automatic video summarization algorithms and how to produce more “representative” video summaries. The research presented is from the FAIRview project and is supported by the Digital News Innovation Fund (DNI Fund), which is part of the Google News Initiative.

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FAIRview: Responsible Video Summarization @NYCML'18

  1. 1. FAIRview: Responsible Video Summarization / Lora Aroyo / Tagasauris Inc / http://lora-aroyo.org / @laroyo
  2. 2. Agenda for today 3:00 - 3:20: Introduction of Video Summarization Context 3:20 - 3:50: Work in groups to answer the following questions (discussion document: http://bit.ly/fairview_discussion) Q1: How to increase the user awareness (e.g. through explanations, visualizations, interaction, etc) on the following two points: ○ the video summary “representativeness” compared to the original video ○ the (possible) video summary “misinformation potential” compared to the original video Q2: What are adequate success metrics for video summaries? ○ How to measure the ‘representativeness’? ○ How to measure ‘misinformation potential’? ○ How to evaluate both points? Answer these questions in the following interaction scenarios: ● while watching the video summary ● when browsing video search results ● when comparing two or more video summaries ● when creating the video summary ● other interaction scenarios 3:50 - 4:00: Summary and conclusions http://lora-aroyo.org https://www.slideshare.net/laroyo @laroyo
  3. 3. video is 64% of Internet traffic http://lora-aroyo.org https://www.slideshare.net/laroyo @laroyo
  4. 4. more Americans prefer to watch their news (46%) than to read it (35%) or listen to it (17%) http://www.journalism.org/2016/07/07/pathways-to-news/ http://lora-aroyo.org https://www.slideshare.net/laroyo @laroyo
  5. 5. 300h of video uploaded each min on YouTube alone http://lora-aroyo.org https://www.slideshare.net/laroyo @laroyo
  6. 6. in 2020 it would take a person more than 5 million years to watch the videos uploaded in a month http://lora-aroyo.org https://www.slideshare.net/laroyo @laroyo
  7. 7. at some point it all looks the same http://lora-aroyo.org https://www.slideshare.net/laroyo @laroyo
  8. 8. … tons of videos but difficult to choose what to watch http://lora-aroyo.org https://www.slideshare.net/laroyo @laroyo
  9. 9. how to make videos consumable in the age of information overload & declining attention span?
  10. 10. Slide credit: @jess3 @slideshare http://lora-aroyo.org https://www.slideshare.net/laroyo @laroyo
  11. 11. … video snacks = the new attention economy? http://lora-aroyo.org https://www.slideshare.net/laroyo @laroyo
  12. 12. … opportunities of snackable content http://lora-aroyo.org https://www.slideshare.net/laroyo @laroyo
  13. 13. … e.g. personalized thumbnails & previews http://lora-aroyo.org https://www.slideshare.net/laroyo @laroyo
  14. 14. … e.g. 4-thumbnail summary in video search results http://lora-aroyo.org https://www.slideshare.net/laroyo @laroyo
  15. 15. http://lora-aroyo.org https://www.slideshare.net/laroyo @laroyo …e.g. bumper ads & previews in video search results
  16. 16. … e.g. micro-moments in video for on-demand discovery search http://lora-aroyo.org https://www.slideshare.net/laroyo @laroyo
  17. 17. … e.g. contextualized hyperlinks in video for direct engagement http://lora-aroyo.org https://www.slideshare.net/laroyo @laroyo
  18. 18. … creating bite size info nuggets (video snacks) that can quickly be consumed, understood & shared http://lora-aroyo.org https://www.slideshare.net/laroyo @laroyo
  19. 19. Let’s look at an example http://lora-aroyo.org https://www.slideshare.net/laroyo @laroyo
  20. 20. Scenes from HBO Series: Big Little Lies http://lora-aroyo.org https://www.slideshare.net/laroyo @laroyo
  21. 21. Scenes from 1 Episode http://lora-aroyo.org https://www.slideshare.net/laroyo @laroyo
  22. 22. Selected: 1 Scene Selected: 1 Scene http://lora-aroyo.org https://www.slideshare.net/laroyo @laroyo
  23. 23. Frames for the selected sceneSelected: 1 Scene http://lora-aroyo.org https://www.slideshare.net/laroyo @laroyo
  24. 24. All Concepts describing the FrameSelected: 1 Frame http://lora-aroyo.org https://www.slideshare.net/laroyo @laroyo
  25. 25. all this results in a lot of video, image and label data … that could be organized in lots of different storylines i.e. video snacks http://lora-aroyo.org https://www.slideshare.net/laroyo @laroyo
  26. 26. cars kids nature guns hugs TOPICS The (infinite) stories you can tell with data ... http://lora-aroyo.org https://www.slideshare.net/laroyo @laroyo
  27. 27. cars kids nature guns hugs FORMATS 4-Frames Preview 6-Sec Trailer Adaptive Starting Frames Skimming Static Dynamic The (infinite) stories you can tell with data ... http://lora-aroyo.org https://www.slideshare.net/laroyo @laroyo
  28. 28. cars kids nature guns hugs INTERACTIONS Hyperlinks E-commerce links Looping Autoplay Recommendation Canvas locate buy learnname The (infinite) stories you can tell with data ... http://lora-aroyo.org https://www.slideshare.net/laroyo @laroyo
  29. 29. http://lora-aroyo.org https://www.slideshare.net/laroyo @laroyo
  30. 30. … but how are these video stories created? who selects what to include / exclude? who chooses the summarization approaches? what is the impact of different approaches? http://lora-aroyo.org https://www.slideshare.net/laroyo @laroyo
  31. 31. … all these choices can amplify / diminish a specific aspect or perspective in the original video, and in this way introduce a bias that can potentially lead to misinformation http://lora-aroyo.org https://www.slideshare.net/laroyo @laroyo
  32. 32. FAIRView ● how to bring more awareness of all the perspectives, topics and elements present in the original video ● what are indicators & evaluation criteria on how these are represented in a video summary ● how to adapt existing summarization algorithms to produce representative & explainable video summaries http://lora-aroyo.org https://www.slideshare.net/laroyo @laroyo
  33. 33. FAIRView ● study this problem in the context of news videos ● empower users with tools to evaluate representativeness of videos ● gain a granular understanding of video content in terms of perspectives, opinions, stories, etc. http://lora-aroyo.org https://www.slideshare.net/laroyo @laroyo
  34. 34. Work in Groups 3:20 - 3:50: Work in groups to answer the following questions (discussion document: http://bit.ly/fairview_discussion) Q1: How to increase the user awareness (e.g. through explanations, visualizations, interaction, etc) on the following two points: ○ the video summary “representativeness” compared to the original video ○ the (possible) video summary “misinformation potential” compared to the original video Q2: What are adequate success metrics for video summaries? ○ How to measure the ‘representativeness’? ○ How to measure ‘misinformation potential’? ○ How to evaluate both points? Answer these questions in the following interaction scenarios: ● while watching the video summary ● when browsing video search results ● when comparing two or more video summaries ● when creating the video summary ● other interaction scenarios 3:50 - 4:00: Summary and conclusions http://lora-aroyo.org https://www.slideshare.net/laroyo @laroyo
  35. 35. FAIRview: Responsible Video Summarization / Lora Aroyo / Tagasauris Inc / http://lora-aroyo.org / @laroyo

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