A Chinese abstract generation method and device based on a generative adversarial network
A technology of abstract and Chinese, applied in the field of Chinese abstract generation based on generative adversarial network, can solve the problem of inconsistency of actual evaluation indicators of optimization methods, and achieve the effect of reducing the appearance of unregistered words, high performance, and reducing dictionary
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Publication Date
- 2019-05-17
Smart Images

Figure 1 
Figure 2 
Figure 3
Abstract
Description
technical field
[0001] The invention belongs to the technical field of artificial intelligence and deep learning, and in particular relates to a method and device for generating a Chinese abstract based on a generative confrontation network. Background technique
[0002] With the advent of the era of big data, Internet information is growing exponentially, especially text information. How to quickly obtain key information from redundant texts is very important. However, constructing summaries manually is expensive and impractical. Therefore, it is of practical value to construct an automatic summarization system with low cost, large scale and high efficiency.
[0003] The current Chinese summarization methods can be divided into "extractive summarization" and "generative summarization". Extractive summary methods include classification-based Bayesian, maximum entropy, and SVM, and graph-based TextRank and LexRank methods. Since generative summarization is generated based...
Examples
Embodiment Construction
[0043] In order to make the above objects, features and advantages of the present invention more comprehensible, the present invention will be further described in detail below through specific embodiments and accompanying drawings.
[0044] In the method for generating a Chinese abstract based on a generative confrontation network in this embodiment, the abstract generation process is as follows figure 1 shown, including the following steps:
[0045] Step 1. Perform data preprocessing operations such as word segmentation, stop words removal, and special word marking on the given Chinese data set, and divide the data into training set, verification set and test set after shuffling.
[0046] Step 2, build a Chinese abstract generation model based on GAN, and use the training set in step 1 to train the Chinese abstract generation model.
[0047] Step 3: After the training of the Chinese summary generation model is completed, use the test set to test the performance of the model...