A Multimodal Analytics Platform for Journalists Analyzing Large-Scale, Heterogeneous Multilingual, and Multimedia Content - Vrochidis, Stefanos; Moumtzidou, Anastasia; Gialampoukidis, Ilias; Liparas, Dimitris; Casamayor, Gerard; Wanner, Leo; Heise, Nicolaus; Wagner, Tilman; Bilous, Andriy; Jamin, Emmanuel; Simeonov, Boyan; Alexiev, Vladimir; et al. (Frontiers in Robotics and AI, 2018)
Analysts and journalists face the problem of having to deal with very large, heterogeneous and multilingual data volumes that need to be analyzed, understood and aggregated. Huge savings could be made in terms of time, labor and costs by automating and simplifying their editorial and authoring processes. Therefore, there is a need for technologies that provide unified access to multilingual and multicultural economic, news story material across borders, that ensure its context-aware, spatiotemporal, and semantic interpretation, and that correlate and summarize the interpreted material into a coherent gist. In this paper, we present a platform integrating multimodal analytics techniques, which are able to support journalists in handling large streams of real-time and diverse information. Specifically, the platform automatically crawls and indexes multilingual and multimedia information from heterogeneous resources. Textual information is automatically summarized and can be translated (on demand) into the language of the journalist. High-level information is extracted from both textual and multimedia content for fast inspection using concept clouds. The textual and multimedia content is semantically integrated and indexed using a common representation, to be accessible through a web-based search engine. The evaluation of the proposed platform was performed by several groups of journalists revealing great satisfaction from the user side.