A method for automatic extraction of document summaries based on word vectors
An automatic extraction and document summarization technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as ignoring the semantic similarity between sentences, reducing the accuracy of node weights, and affecting the performance of summarization, so as to avoid Computing error issues, improving accuracy and effects of summarization performance
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[0085] In order to make the purpose, technical solutions and beneficial effects of the present invention clearer and easier to implement, the present invention will be further described in detail in combination with the following specific embodiments and with reference to the accompanying drawings. In this embodiment, the length of the generated summary is preset to be 150 words.
[0086] S1. Use the deep neural network model to train the corpus to obtain the word vector representation of the feature words:
[0087] In order to obtain the vector representation of feature words, the embodiment adopts the biomedical literature database MEDLINE maintained by the National Library of Medicine of the United States to collect the corpus used for the experiment. Preprocess the sentences in the citation, that is, remove stop words, special characters, and punctuation marks against the stop word list, and finally obtain a 1.2G training corpus.
[0088] In the training process of this e...
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