Abstract-Automatic text summarization aims to address the information overload problem by extracting the most important information from a document, which can help a reader to decide whether it is relevant or not. In this paper we propose a method of personalized text summarization which improves the conventional automatic text summarization methods by taking into account the differences in readers' characteristics. We use annotations added by readers as one of the sources of personalization. We have experimentally evaluated the proposed method in the domain of learning, obtaining better summaries capable of extracting important concepts explained in the document when considering the relevant domain terms in the process of summarization.

-Automatic image annotation methods require a quality training image dataset, from which annotations for target images are obtained. At present, the main problem with these methods is their low effectiveness and scalability if a large-scale training dataset is used. Current methods use only global image features for search. We proposed a method to obtain annotations for target images, which is based on a novel combination of local and global features during search stage. We are able to ensure the robustness and generalization needed by complex queries and significantly eliminate irrelevant results. In our method, in analogy with text documents, the global features represent words extracted from paragraphs of a document with the highest frequency of occurrence and the local features represent key words extracted from the entire document. We are able to identify objects directly in target images and for each obtained annotation we estimate the probability of its relevance. During search, we retrieve similar images containing the correct keywords for a given target image. For example, we prioritize images where extracted objects of interest from the target images are dominant as it is more likely that words associated with the images describe the objects. We tailored our method to use large-scale image training datasets and evaluated it with the Corel5K corpus which consists of 5000 images from 50 Corel Stock Photo CDs.

With the proliferation of mobile devices, management of the growing user personal generated multimedia content is more demanding. Proper organization of this content requires manual metadata authoring, since automated or crowdsourcing approaches are inapplicable in case of personal content or content of a small social group (e.g. family). Recently, games with a purpose gained popularity in solving many human intelligence tasks, with main focus drawn onto resource metadata and semantics acquisition. Games with a purpose seem to have large potential for solving further problems, but they also face several design issues involving mainly the validation of human-created artifacts they provide. In this paper we analyze these issues and propose directions for overcoming them for the semantics acquisition domain. Furthermore, we propose a method for annotating and presenting personal multimedia content based on our previously developed game with a purpose, which also exploits the alternative artifact evaluation.