VIKOR Algorithm Based on Cuckoo Search for Multidocument Text Summarization Due to the huge amount of documents on the internet and the redundancy contained in each document makes it difficult for the user to get useful information. Automatic text summarization is a solution to such problems of information overload. Text summarization is the process of generating a single document summary from a set of documents or from a single document. This paper proposes a method based on the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) algorithm. In the first stage based on extracting six features for each sentence in the document collection, the next cuckoo search algorithm was applied to compute weights for these extracted features. Finally, the VIKOR algorithm used to rank the sentences. The sentences with a high score and less redundancy selected to be included in the final summary. The proposed model is evaluated by using the dataset supplied by the Text Analysis Conference (TAC2011) for English documents. The performance of the proposed model is evaluated using the RecallOriented Understudy for Gisting Evaluation (ROUGE) metric. The obtained results support the effectiveness of the proposed model.

Multidocument Arabic Text Summarization Based on Clustering and Word2Vec to Reduce Redundancy Arabic is one of the most semantically and syntactically complex languages in the world. A key challenging issue in text mining is text summarization, so we propose an unsupervised scorebased method which combines the vector space model, continuous bag of words (CBOW), clustering, and a statisticallybased method. The problems with multidocument text summarization are the noisy data, redundancy, diminished readability, and sentence incoherency. In this study, we adopt a preprocessing strategy to solve the noise problem and use the word2vec model for two purposes, first, to map the words to fixedlength vectors and, second, to obtain the semantic relationship between each vector based on the dimensions. Similarly, we use a kmeans algorithm for two purposes: (1) Selecting the distinctive documents and tokenizing these documents to sentences, and (2) using another iteration of the kmeans algorithm to select the key sentences based on the similarity metric to overcome the redundancy problem and generate the initial summary. Lastly, we use weighted principal component analysis (WPCA) to map the sentencesxe2x80x99 encoded weights based on a list of features. This selects the highest set of weights, which relates to important sentences for solving incoherency and readability problems. We adopted RecallOriented Understudy for Gisting Evaluation (ROUGE) as an evaluation measure to examine our proposed technique and compare it with stateoftheart methods. Finally, an experiment on the Essex Arabic Summaries Corpus (EASC) using the ROUGE1 and ROUGE2 metrics showed promising results in comparison with existing methods.