# Hybridization of Intelligent Solutions Architecture for Text Understanding and Text Generation
## 2 State-of-the-Art
Neural networks with short-term memory or the forgetting effect can capture and memorize dependencies between terms, presenting good results. Additional information helpful for text analysis can be obtained by clustering nouns and verbs following their meanings [15]. Applying this approach requires building a vocabulary containing word roots and endings that give a grammatical feature to words. Vocabulary is used to build a network of word meanings. The network reflects the relationship between lexemes of nouns and verbs, all of which are hierarchically combined into groups that have specific features and common properties. Understanding the semantic correlations between the text and associated images, video and audio recordings, geotags, etc., has great potential for improving text understanding systems. However, understanding multimedia information still remains an unsolved research problem, although there are already some successes in this area [16]. Implementation of algorithms of big data and deep learning combined with enormous computational capabilities support the technical progress in the field of text understanding [17]. The essence of such approaches is to combine semantic and big data-based representations to more accurately identify the meaning. The evaluations of these approaches show that they significantly outperform traditional benchmarks. The capabilities of automatic text understanding are also important for solving the problems of standard documents' development and processing. The authors of [18] show that neural networks easily generate texts, but the resulting passages cannot come close to human-created documentation. The use of templates, copying and modification solves this problem and significantly improves the generation results. Thus, most research in the field of text generation does not take into account the context and structure of the text that should be obtained. The research in [19] presents the architecture of a neural network, which includes the selection and planning of content and leads to the division of the generation problem into two stages: first, a content plan is created and it is highlighted which information should be mentioned and in what order, and then a document is already generated taking into account this plan. A new text generation system is proposed in [20]. This system creates a new story based on a series of stories entered. Two options for generating new stories were considered: based on stories with different plots and characters, and based on the same stories, but from different volumes of the book, where the storyline is in the same context and the characters are similar. The results obtained by the system are analyzed according to the following parameters: the correctness of grammar, the relationship of events, the level of interest and uniqueness. One of the tasks of text generation is to compare the content of existing documents. Content analysis is used to find recommended documents with similar semantic content. In [21], a new knowledge-based technique for calculating inter-document similarity is proposed, called semantic context analysis. Reference [22] presents an in-depth study of the technology for calculating the similarity of documents. Based on the analysis of the state-of-the-art and some experience in the area of semantic analysis and enterprise content management [23; 24; 25], we have identified a gap in intelligent text understanding and text generation application to practice. This gap consists of the lack of adaptive software architecture capable of combination and extension of artificial intelligent solutions according to the developing situations and changing problem statements, without retraining of the neural networks. To solve this problem, a new approach was developed for intelligent text analysis and text generation applications.