Extractive text summarization aims to create a condensed version of one or more source documents by selecting the most informative sentences. Research in text summarization has therefore often focused on measures of the usefulness of sentences for a summary. We present an approach to sentence extraction that maps sentences to nodes of a hierarchical ontology. By considering ontology attributes we are able to improve the semantic representation of a sentence's information content. The classifier that maps sentences to the taxonomy is trained using search engines and is therefore very flexible and not bound to a specific domain. In our experiments, we train an SVM classifier to identify summary sentences using ontology-based sentence features. Our experimental results show that the ontology-based extraction of sentences outperforms baseline classifiers, leading to higher Rouge scores of summary extracts.
Compared to only a few years ago, today there is an abundance of annotated image data available on the Internet. For researchers on image retrieval, this is an unforseen but welcome consequence of the rise of Web 2.0 technologies. Popular social networking and content sharing services seem to hold the key to the integration of context and semantics into retrieval. However, at least for now, it appears that this promise has to be taken with a grain of salt. In this paper, we present preliminary empirical results on the tagging behavior of power users of content sharing and social bookmarking services. Our findings suggest different promising research directions for image retrieval and we briefly discuss some of them.
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