Text summarization is a process that reduces the size of the text document and extracts significant sentences from a text document. We present a novel technique for text summarization. The originality of technique lies on exploiting local and global properties of words and identifying significant words. The local property of word can be considered as the sum of normalized term frequency multiplied by its weight and normalized number of sentences containing that word multiplied by its weight. If local score of a word is less than local score threshold, we remove that word. Global property can be thought of as maximum semantic similarity between a word and title words. Also we introduce an iterative algorithm to identify significant words. This algorithm converges to the fixed number of significant words after some iterations and the number of iterations strongly depends on the text document. We used a two-layered backpropagation neural network with three neurons in the hidden layer to calculate weights. The results show that this technique has better performance than MS-word 2007, baseline and Gistsumm summarizers
k-degree anonymity is known as one of the best models for anonymizing social network graphs. Although recent works have tried to address the privacy challenges of social network graphs, privacy levels are considered to be independent of the features of the graph degree sequence. In other words, the optimal value of k is not considered for the graph, leading to increasing information loss. Additionally, the graph may not need a high privacy level. In addition, determining the optimal value of k for the graph in advance is a big problem for the data owner. Therefore, in this paper, we present a technique named FSopt_k that is able to find the optimal value of k for each social network graph. This algorithm uses an efficient technique to partition the graph nodes to choose the best k value. It considers the graph structure features to determine the best privacy level. In this way, there will be a balance between privacy and loss in the anonymized graph. Furthermore, information loss will be as low as possible. The evaluation results depict that this algorithm can find the optimal value of k in a short time as well as preserve the graph’s utility.
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