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. Automated and simplified editorial and authoring process could significantly reduce time, labor, and costs. Therefore, there is a need for unified access to multilingual and multicultural news story material, beyond the level of a nation, ensuring context-aware, spatiotemporal, and semantic interpretation, correlating also and summarizing 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 satisfaction from the user side.
%0 Journal Article
%1 Vrochidis2018-Multimodal
%A Vrochidis, Stefanos
%A Moumtzidou, Anastasia
%A Gialampoukidis, Ilias
%A Liparas, Dimitris
%A Casamayor, Gerard
%A Wanner, Leo
%A Heise, Nicolaus
%A Wagner, Tilman
%A Bilous, Andriy
%A Jamin, Emmanuel
%A Simeonov, Boyan
%A Alexiev, Vladimir
%A Busch, Reihard
%A Arapakis, Ioannis
%A Kompatsiaris, Ioannis Yiannis
%D 2018
%J Frontiers in Robotics and AI
%K analyst analytics_plaforms journalist multimedia multimodal multinlingual semantic_indexing summarization web_crawling
%R 10.3389/frobt.2018.00123
%T A multimodal analytics platform for journalists analysing large-scale, heterogeneous multilingual and multimedia content
%U https://doi.org/10.3389/frobt.2018.00123
%V 5
%X 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. Automated and simplified editorial and authoring process could significantly reduce time, labor, and costs. Therefore, there is a need for unified access to multilingual and multicultural news story material, beyond the level of a nation, ensuring context-aware, spatiotemporal, and semantic interpretation, correlating also and summarizing 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 satisfaction from the user side.
@article{Vrochidis2018-Multimodal,
abstract = {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. Automated and simplified editorial and authoring process could significantly reduce time, labor, and costs. Therefore, there is a need for unified access to multilingual and multicultural news story material, beyond the level of a nation, ensuring context-aware, spatiotemporal, and semantic interpretation, correlating also and summarizing 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 satisfaction from the user side.},
added-at = {2021-08-25T16:07:36.000+0200},
author = {Vrochidis, Stefanos and Moumtzidou, Anastasia and Gialampoukidis, Ilias and Liparas, Dimitris and Casamayor, Gerard and Wanner, Leo and Heise, Nicolaus and Wagner, Tilman and Bilous, Andriy and Jamin, Emmanuel and Simeonov, Boyan and Alexiev, Vladimir and Busch, Reihard and Arapakis, Ioannis and Kompatsiaris, Ioannis Yiannis},
biburl = {https://www.bibsonomy.org/bibtex/24d101485a80d36aad4216fe9a2a352f8/valexiev},
doi = {10.3389/frobt.2018.00123},
eissn = {2296-9144},
interhash = {3bbde3c723a72d2624ec0a74cfba929c},
intrahash = {4d101485a80d36aad4216fe9a2a352f8},
issn = {2296-9144},
journal = {Frontiers in Robotics and AI},
keywords = {analyst analytics_plaforms journalist multimedia multimodal multinlingual semantic_indexing summarization web_crawling},
month = oct,
timestamp = {2021-08-25T16:07:36.000+0200},
title = {A multimodal analytics platform for journalists analysing large-scale, heterogeneous multilingual and multimedia content},
topic = {Machine Learning at Scale: How Big Data and AI are Transforming Engineering},
url = {https://doi.org/10.3389/frobt.2018.00123},
volume = 5,
year = 2018
}