Text clustering multi-document automatic abstracting method and system for improving word vector model
A text clustering and automatic summarization technology, applied in neural learning methods, biological neural network models, character and pattern recognition, etc., can solve problems such as blunt contextual connection, grammatical errors in summaries, and logical incoherence
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[0080] Below, in conjunction with accompanying drawing and specific embodiment, the invention is further described:
[0081] see Figure 1-5 , according to an embodiment of the present invention, a text clustering multi-document automatic summarization method and system for improving the word vector model, the steps are as follows
[0082] The first step: preprocessing;
[0083] The second step: improve word vector model training;
[0084] The third step: sentence vector representation and clustering;
[0085] Step 4: Extract article summary sentences and generate abstracts;
[0086] The preprocessing method of the first step is: (1) Chinese word segmentation, the text sentence after the word segmentation processing is divided into word units with independent segmentation and processing meaning, and the corpus after the text word segmentation processing can be used for word vector training. The jieba word segmentation tool performs text segmentation on the corpus;
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