Fusion of sentence embeddings for news retrieval

The availability of a vast quantity of information from news channels and social media, make it often difficult to find and follow specific events. This applies to both casual readers and to intelligence and emergency response analysts. In particular, the latter need to find and process relevant information within sense-making, situation and impact assessment processes. The automatic retrieval and tracking of news has been addressed by a good number of works in the information retrieval literature. However, there is a strong potential for introducing automatic systems employing information fusion methods and techniques to assist decision makers. In the field of deep learning, several techniques for text encoding have been proposed, which have allowed significant progress also in the field of news retrieval and ranking. The objective of this paper is to explore the usage and combination of different pre-trained sentence embeddings, including multimodal ones, obtained from different parts of text that compose a news story. This in order to understand which type of technique is best for encoding the different information available in online news.