News production is characterized by complex and dynamic workflows in which it is important to produce and distribute news items as fast as possible. In this paper, we show how personalized distribution and consumption of news items can be enabled by automatically enriching news metadata with open linked datasets available on the Web of data, thus providing a more pleasant experience to fastidious consumers where news content is presented within a broader historical context. Further we present a faceted browser that provides a convenient way for exploring news items based on an ontology of NewsML-G2 and rich semantic metadata.

Broadcast video retrieval is a key issue for media researchers looking for suitable media material in archives. Current media retrieval applications in use at VRT have proven to be a suboptimal solution. In this paper, we explain a novel search environment based on the combined metadata from multiple broadcast systems. Furthermore, we explain how speech recognition can facilitate unlocking the archive. This is illustrated by conducting experiments and measuring the keyword recognition rate, rather than the pure word error rate. We show that the keyword recognition rate is sufficient for efficient media retrieval in a search application.

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This paper reports on a user-experience study undertaken as part of the H2020 project MeMAD ('Methods for Managing Audiovisual Data: Combining Automatic Efficiency with Human Accuracy'), in which multimedia content describers from the television and archive industries tested Flow, an online platform, designed to assist the post-editing of automatically generated data, in order to enhance the production of archival descriptions of film content. Our study captured the participant experience using screen recordings, the User Experience Questionnaire (UEQ), a benchmarked interactive media questionnaire and focus group discussions, reporting a broadly positive post-editing environment. Users designated the platform's role in the collation of machine-generated content descriptions, transcripts, named-entities (location, persons, organisations) and translated text as helpful and likely to enhance creative outputs in the longer term. Suggestions for improving the platform included the addition of specialist vocabulary functionality, shot-type detection, film-topic labelling, and automatic music recognition. The limitations of the study are, most notably, the current level of accuracy achieved in computer vision outputs (i.e. automated video descriptions of film material) which has been hindered by the lack of reliable and accurate training data, and the need for a more narratively oriented interface which allows describers to develop their storytelling techniques and build descriptions which fit within a platform-hosted storyboarding functionality. While this work has value in its own right, it can also be regarded as paving the way for the future (semi)automation of audio descriptions to assist audiences experiencing sight impairment, cognitive accessibility difficulties or for whom 'visionless' multimedia consumption is their preferred option.