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Content-Based Keywords Extraction and Automatic Advertisement Associations to Multimodal News Aggregations

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Part of the Studies in Computational Intelligence book series (SCI, volume 439)

Abstract

Nowadays, Web is characterized by a growing availability of multimedia data together with a strong need for integrating different media and modalities of interaction. Hence, one of the main challenges is to bring into the Web data thought and produced for different media, such as TV or press content. In this scenario, we focus on multimodal news aggregation retrieval and fusion. Multimodality, here, is intended as the capability of processing, gathering, manipulating, and organizing data from multiple media. In particular, we tackle two main issues: to extract relevant keywords to news and news aggregations, and to automatically associate suitable advertisements to aggregated data. To achieve the first goal, we propose a solution based on the adoption of extraction-based text summarization techniques; whereas to achieve the second goal, we developed a contextual advertising system that works on multimodal aggregated data. To assess the proposed solutions, we performed experiments on Italian news aggregations. Results show that, in both cases, the proposed solution performs better than the adopted baseline solutions.

Keywords

News Story Information Fusion Reference Scenario Statistical Machine Translation Text Summarization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Dept.of Electrical and Electronic EngineeringUniversity of CagliariCagliariItaly
  2. 2.RAI Centre for Research and Technological InnovationTorinoItaly

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