This paper describes a collaboration between ±«Óãtv News and ±«Óãtv Research & Development which is exploring ways of using the ±«Óãtv’s linear programme output and associated production metadata, along with artificial intelligence and machine learning techniques, to segment the programmes into individual stories. These segments can then be used to provide chapterised versions of the linear output, make individual stories findable by search and available as items on web pages. Ultimately, by using object-based media techniques these stories can be recombined to provide personalised and/or interactive experiences.
This paper begins by outlining our hypothesis that audiences will gain more value from our linear output by giving access to individual stories and items compared to entire, undifferentiated programmes and outline the user testing that supports it. It explains the approach being taken to recover programme metadata and how that can help achieve content segmentation at scale. The paper then describes how object-based techniques are being used to automatically combine segmented content to make new interactive and personalised experiences, including the prototype code base and the workflows developed so far. The paper concludes with a summary of the key findings from the work to date, including audience benefits, data consistency and the role of editorial oversight.
This paper was originally published at IBC 2021.
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