Biblio

Automatic extractive text summarization is the process of condensing textual information while preserving the important concepts. The proposed method after performing pre-processing on input Persian news articles generates a feature vector of salient sentences from a combination of statistical, semantic and heuristic methods and that are scored and concatenated accordingly. The scoring of the salient features is based on the article's title, proper nouns, pronouns, sentence length, keywords, topic words, sentence position, English words, and quotations. Experimental results on measurements including recall, F-measure, ROUGE-N are presented and compared to other Persian summarizers and shown to provide higher performance.

Now a days transferring of texts, documents over the internet are the tasks in common. The transferred text must be cryptographically protected so that cannot be accessed by the invaders. In the communication medium, protected data uses cryptographic techniques and random bit generators. Once the key is generated by the random generators, how well we can secure and transmit fast in the network plays a vital role by applying appropriate algorithm. As a solution, a system is developed by symmetric algorithmic approach, uses AES and Fiestel content and also implements three different ways of random generators such as pseudorandom number generator (PRNG), linear multiples of prime sequence based method and nonlinear prime methods. Multilevel encryption and decryption techniques are adopted in the solution to transfer the information over the network securely with reduced delay. This method provides very strong technique against different kinds of attacks.