Aviation fault text abstract generation method and device based on fault causal atlas

Abstract

The invention discloses an aviation fault text abstract generation method and device based on a fault causal atlas. The method comprises the following steps: step 1, generating a directory atlas through an original fault causal atlas; 2, obtaining a feature vector of a target text sample; 3, generating text candidate abstracts by using the fault causal atlas model; 4, decoding the text generation model; and 5, generating an aviation fault text abstract. According to the method, firstly, a catalog atlas is obtained for a fault causal atlas which is constructed in advance through an agglomerated hierarchical clustering method, then structural features obtained in advance are introduced in the encoding stage and the decoding stage through the fault causal atlas, and finally the needed aviation fault text abstract is generated. According to the method, information extraction is carried out by utilizing a document chapter structure, fault logic positioning is carried out by applying a fault cause and effect graph, and the problem that an existing generative abstract model cannot meet chapter directory structure constraints is solved.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to a method and device for generating aviation fault text summaries based on fault causal graphs. Background technique [0002] The text summarization model can convert long text paragraphs into condensed summary texts, and preserve all the information in the original text as much as possible. Generative summarization is usually a generative language model that uses large-scale corpus pre-training. Input a piece of text as the beginning and continue to write the text through the auto-regression mechanism. After fine-tuning on the summary data, input the original text as the beginning, and the model will generate Abstract of the original text. However, this kind of generative summary model only satisfies the fluent grammar, but when it is applied to the aviation failure field text, the chapter structure of the generated content will be unreasonable. For example, in the n...

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Problems solved by technology

However, this kind of generative summary model only satisfies the fluent grammar, but when it is applied to the aviation failure field text, the chapter structure of the generated content will be unreasonable. For example, in the narrative process, the root cause and secondary cause, The sequence of different types of events in the fault occurrence process is reversed, and problems such as logical confusion will occur

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