Generating Method of Blog Text Summarization Based on Deep Learning

Abstract

The invention discloses a blog text abstract generation method based on deep learning, comprising the following steps: crawling blog data; preprocessing the crawled blog data, selecting blog text data; and using the selected blog text data according to Chinese word vectors Convert the dictionary into vector matrix data; build a deep learning encoder-decoder (encoder-decoder) model, and train the encoder encoder and decoder decoder of the model separately, and use it after the training is completed; repeat steps S01-S03 to generate Data, which will generate a summary of predictions from the model that the data has completed training. The present invention automatically generates blog text summaries based on the deep learning framework encoder-decoder, and at the same time can obtain deeper semantic links of blogs. The generated text summary can intuitively display the main content of the current blog, and has a wide application prospect.

Description

technical field [0001] The present invention relates to a method for generating text abstracts, in particular to a method for generating blog text abstracts based on deep learning. Background technique [0002] Natural Language Processing (NLP) is a particularly important part of artificial intelligence. It includes multiple sub-tasks such as text classification, sentiment analysis, machine translation, and reading comprehension. Almost one sub-task is a very important professional research field. are independent and interrelated. [0003] Deep learning is a new type of end-to-end learning method proposed in recent years. In ordinary processing tasks such as classification, the effect of ordinary neural networks may be almost the same, but in the process of high-dimensional data calculation and feature extraction, depth Learning to use deep networks to fit shows its powerful computational capabilities. At present, deep learning has been applied to many fields-image process...

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Problems solved by technology

[0005] The generation of text summaries in natural language is mainly divided into two methods: the first extraction type, text summaries based on rules and statistics, has been proved by a large number of practical applications; the second is abstract type, based on deep learning model summarization generation, It has been greatly improved in 2014, from mechanical text summarization to comprehensible text summarization generation, currently using the encoder-decoder framework and embedding a recurrent neural network to achieve it, the application in Chinese is not obvious

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