Abstract
We present an Information Retrieval tool that facilitates the task of the user when searching for a particular information that is of interest to him. Our system processes a given set of documents to produce a graph, where nodes represent documents and links the similarities. The aim is to offer the user a tool to navigate in this space in an easy way. It is possible to collapse/expand nodes. Our case study shows affinity groups based on the similarities of text production of researchers. This goes beyond the already established communities revealed by co-authorship. The system characterizes the activity of each author by a set of automatically generated keywords and by membership to a particular affinity group. The importance of each author is highlighted visually by the size of the node corresponding to the number of publications and different measures of centrality. Regarding the validation of the method, we analyse the impact of using different combinations of titles, abstracts and keywords on capturing the similarity between researchers.

Abstract
Several algorithms based on PageRank algorithm have been proposed to rank the document sentences in the multi-document summarization field and LexRank and T-LexRank algorithms are well known examples. In literature different concepts such as weighted inter-cluster edge, cluster-sensitive graph model and document-sensitive graph model have been proposed to improve LexRank and T-LexRank algorithms (e.g. DsR-G, DsR-Q) for multi-document summarization. In this paper, a density-based graph model for multi-document summarization is proposed by adding the concept of density to LexRank and T-LexRank algorithms. The resulting generic multi-document summarization systems, DensGS and DensGSD were evaluated on DUC 2004 while the query-based variants, DensQS, DensQSD were evaluated on DUC 2006, DUC 2007 and TAC 2010 task A. ROUGE measure was used in the evaluation. Experimental results show that density concept improves LexRank and T-LexRank algorithms and outperforms previous graph-based models (DsR-G and DsR-Q) in generic and query-based multi-document summarization tasks. Furthermore, the comparison of the number of iterations indicates that the density-based algorithm is faster than the other algorithms based on PageRank.

Abstract
Most research on multi-document summarization explores methods that generate summaries based on queries regardless of the users' preferences. We note that, different users can generate somewhat different summaries on the basis of the same source data and query. This paper presents our study on how to exploit the information regards how users summarized their texts. Models of different users can be used either separately, or in an ensemble-like fashion. Machine learning methods are explored in the construction of the individual models. However, we explore yet another hypothesis. We believe that the sentences selected into the summary should be coherent and supplement each other in their meaning. One method to model this relationship between sentences is by detecting actor-object relationship (AOR). The sentences that satisfy this relationship have their importance value enhanced. This paper combines ensemble summarizing system and AOR to generate summaries. We have evaluated this method on DUC 2006 and DUC 2007 using ROUGE measure. Experimental results show the supervised method that exploits the ensemble summarizing system combined with AOR outperforms previous models when considering performance in query-based multi-document summarization tasks.

Abstract

Abstract
Brochures are a versatile and ubiquitous tourism advertising medium. Although almost all types of advertisement message strategies are considered in brochure development and production, the relationship of those strategies with brand destination attributes has not been studied. Likewise an extensive inventory of executional tactics can be applied to put forward a brochure concept. This research shows the relationship among executional tactics, message strategies and destination attributes in 400 tourism brochures from around the world. On average, each message strategy is associated with seventeen executional tactics. A single brand destination attribute may work with many message strategies. However, the connection between destination attributes and executional tactics is rather loose. This media-centered approach study will provide a benchmarking profile to advertising agency and tourism destination managers in their endeavor to develop brochures.

Abstract
The objective of this study is to explore the effectiveness of three digital shopping platforms (Plain Interactive, Marker-based Augmented Reality and Markerless Augmented Reality), on the impressions and purchase intentions of consumers. The study is mainly interested in analysing whether intelligent shopping platforms with AR elements provide any added advantage to an advertised product in the form of favourable attitude or a stronger purchase impulse. During the tests with the three shopping platforms, quantitative data was collected via computerised questionnaire. High and Low class users were statistically extracted, corresponding to the high or low probability to buy or recommend the advertised brand. The results show that Markerless AR system clearly outperforms the Marker-based AR and the Plain Interactive in terms of positive attitude from the users. The second better performing system is the Marker-based AR, which closely follows the Markerless AR, while the Plain Interactive system obtains least approval.