Text brief report generation method and device, electronic equipment and readable storage medium

Abstract

The invention relates to the artificial intelligence technology, and discloses a text brief report generation method comprising the following steps: using a first brief report set and a first categorylabel set to train a pre-constructed original label classification network so as to obtain a standard label classification network, using the standard label classification network to execute label classification operation on a second brief report set, obtaining a second category label set, summarizing the first category label set and the second category label set to obtain a category label set, dividing the first brief report set and the second brief report set by using the category label set to obtain a category brief report set, generating a brief report template by using the category briefreport set, receiving a brief report text to be generated, and executing named entity identification on the brief report text to be generated to obtain an entity set, and executing a combination operation on the entity set and the brief report template to obtain a brief report. The invention further provides a text brief report generation device, electronic equipment and a computer readable storage medium. According to the invention, the calculation resource can be saved, and the brief report generation accuracy is improved.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence, and in particular to a method, device, electronic equipment, and computer-readable storage medium for generating a text bulletin. Background technique [0002] A briefing is short information that conveys information, also known as a "short message". At present, text briefing generation methods mainly include extractive briefing generation method and generative briefing generation method. [0003] The extractive presentation generation method first performs text vectorization, calculates the similarity between each group of text vectors and stores them in a matrix, and uses the matrix to construct a graph structure to generate a presentation; the generative presentation generation method inputs the vectorized text to the encoder - A deep neural network with a decoder structure, after iteratively training the internal parameters of the deep neural network, use the network o...

AI Technical Summary

Problems solved by technology

[0004] Both of the above two methods can generate presentations, but the extractive presentation generation method relies only on simple mathematical calculations for vector similarity, the similarity results are highly random, and the quality of presentations needs to be improved. The extraction presentation generation method needs to rely on a large amount of training data to train the neural network The network and the labeling of the previous training data are not only time-consuming and labor-intensive, but also consume too much computer resources to train the model based on a large amount of training data

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