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By increasing the amount of data in computer networks, searching and finding suitable information will be harder for users. One of the most widespread forms of information on such networks are textual documents. So exploring these documents to get information about their content is difficult and sometimes impossible. Multi-document text summarization systems are an aid to producing a summary with a fixed and predefined length, while covering the maximum content of the input documents. This paper presents a novel method for multi-document extractive summarization based on textual entailment relations and sentence compression via formulating the problem as a knapsack problem. In this approach, sentences of documents are ranked according to the extended Tf-Idf method, then entailment scores of selected sentences are computed. Through these scores, the final score of each sentence is calculated. Finally, by decreasing the lengths of sentences via sentence compression, the problem has been solved by greedy and dynamic Programming approaches to the knapsack problem. Experiments on standard summarization datasets and evaluating the results based on the Rouge system show that the suggested method, according to the best of our knowledge, has increased F-measure of query-based summarization systems by two per cent and F-measure of general summarization systems by five per cent.

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In this paper we consider fuzzy subsets of a universe as L-fuzzy subsets instead of [0, 1]-valued, where L is a complete lattice. We enrich the lattice L by adding some suitable operations to make it into a pseudo-BL algebra.Since BL algebras are main frameworks of fuzzy logic, we propose to consider the non-commutative BL-algebras which are more natural for modeling the fuzzy notions. Based on reasoning with in non-commutative fuzzy logic we model the linguistic modifiers such as very and more or less and give an appropriate membership function for each one by taking into account the context of the given fuzzy notion by means of resemblance L-fuzzy relations.

Abstract-Recognition of emotion from speech is a significant subject in man-machine fields. In this study, speech signal has analyzed in order to create a recognition system which is able to recognize human emotion and a new set of characteristic has proposed in time, frequency and time-frequency domain in order to increase the accuracy. After extracting features of Pitch, MFCC, Wavelet, ZCR and Energy, neural networks classify four emotions of EMO-DB and SAVEE databases. Combination of features for two emotions in EMO-DB database is 100%, for three emotions is 98.48% and for four emotions is 90% due to the variety of speech, existing more spoken words and distinguishing male and female which is better than the result of SAVEE database. In SAVEE database, accuracy is 97.83% for two emotions of happy and sad, 84.75% for three emotions of angry, normal and sad and 77.78% for four emotions of happy, angry, sad and normal