Similarity based text extractive summarization using relevance measure and semantic analysis Available to Purchase

The search and extraction of knowledge is based on fairly basic principles of identifying individual words, analyzing their morphology, statistical patterns and the joint occurrence of linguistic units in a scientific text. One of the most significant and promising areas of semantic analysis is the use of abstracting technologies to automate the process of extracting scientific frames. The implementation of the ontology of frames with accompanying frames with additional information in the form of associative and cause-and-effect relationships is proposed. An algorithm for extractive summarization is developed based on the deterministic selection of relevant information from the segments of the object semantic network. The result of the algorithm is a frame extracted from scientific texts with relation to the reference multiset of keywords. A frame analysis is performed of how the system of scientific knowledge is organized in the reports of the scientific and technical program, in what systemic relations are its concepts.