12. A computer-implemented method for generating annotations, the method being performed on a computer having a processor and a memory, the method for performing the steps of: forming a bidirectional neural network comprising a plurality of layers, the layers including neurons of a first layer corresponding to words (word-neurons), and neurons of a second layer corresponding to sentences (sentence-neurons), the first layer being a layer into which the input query is inputted, and wherein the sentences extracted from documents, the neural network also comprising connections between the neurons, and wherein the word-neurons include additional words, different from the search query, generated by the neural network from the sentences, wherein at least some of the word-neurons of the first layer have connections between each other; activating neurons of the first layer based on a document to be annotated; identifying relevance of the sentence-neurons that correspond to the document, based on the activity of the neurons of the first layer; and in response to a user interactively changing a context of an input query, selecting a subset of the active neurons of the second layer as annotations, such that a sum of activity of the active neurons is above a predetermined threshold, wherein the identified sentence-neurons represent sentences taken from the documents having the highest relevance to document meaning based on a predetermined percentage of document meaning, and that form the annotations of a selected document, wherein the predetermined percentage of document meaning is a function of percentage of meaning of the sentences, and wherein the percentage of meaning of each sentence, describing the degree of relevance of the sentence to the document, is calculated by the bidirectional neural network.
12. A computer-implemented method for generating annotations, the method being performed on a computer having a processor and a memory, the method for performing the steps of: forming a bidirectional neural network comprising a plurality of layers, the layers including neurons of a first layer corresponding to words (word-neurons), and neurons of a second layer corresponding to sentences (sentence-neurons), the first layer being a layer into which the input query is inputted, and wherein the sentences extracted from documents, the neural network also comprising connections between the neurons, and wherein the word-neurons include additional words, different from the search query, generated by the neural network from the sentences, wherein at least some of the word-neurons of the first layer have connections between each other; activating neurons of the first layer based on a document to be annotated; identifying relevance of the sentence-neurons that correspond to the document, based on the activity of the neurons of the first layer; and in response to a user interactively changing a context of an input query, selecting a subset of the active neurons of the second layer as annotations, such that a sum of activity of the active neurons is above a predetermined threshold, wherein the identified sentence-neurons represent sentences taken from the documents having the highest relevance to document meaning based on a predetermined percentage of document meaning, and that form the annotations of a selected document, wherein the predetermined percentage of document meaning is a function of percentage of meaning of the sentences, and wherein the percentage of meaning of each sentence, describing the degree of relevance of the sentence to the document, is calculated by the bidirectional neural network. 15. The method of claim 12 , wherein the input query comprises documents considered relevant by the user.