Unsupervised multi-model fusion extraction type text abstracting method

Abstract

The invention relates to the field of information extraction. The invention discloses an unsupervised multi-model fusion extraction type text abstracting method. An existing extraction type text abstract technology is solved. In order to solve the problems that semantic information of sentences cannot be considered and extracted results cannot accurately and comprehensively describe article contents, the technical scheme of the method is as follows: a centrality text abstract model is trained and optimized in advance, and after optimization, a preprocessed to-be-processed document is calculated to obtain a first batch of abstracts summary1; the preprocessed to-be-processed document is calculated by using a semantic similarity capture model to obtain a second batch of abstract summaty2; thefirst batch of abstracts summary1 and the second batch of abstracts summary2 are fused to obtain a candidate abstract middle_summary2; the MMR algorithm is used to calculate the candidate abstract middle_summery so as to obtain a final abstract final_summery; and semantic understanding and analysis are carried out on text content in a multi-model fusion mode, position information of sentences isfully considered, the importance degree of each sentence can be accurately calculated, and the accuracy, flexibility and diversity of abstract results are improved.

Description

technical field [0001] The invention relates to the field of information extraction, more specifically, it relates to an unsupervised multi-model fusion extractive text summarization method. Background technique [0002] As people's pace of life accelerates, people's patience for text reading also decreases. When people need to read a long piece of news or lengthy academic papers, they often lose their reading patience because the text is too long. Therefore, in order to speed up Reading speed. At present, technologies for intelligently extracting important information from articles have appeared on the market, so that people can quickly understand the key information in the article, save reading time, and improve readers' reading efficiency. [0003] The existing extractive text summarization technology cannot consider the semantic information of the sentence, the extracted result is too single, the information is redundant, some important information is lost, and the extra...

AI Technical Summary

Problems solved by technology

[0003] The existing extractive text summarization technology cannot consider the semantic information of the sentence, the extracted result is too single, the information is redundant, some important information is lost, and the extracted result cannot accurately and comprehensively describe the content of the article

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