Automatic document summarization extraction method based on term vectors

Abstract

Provided is an automatic document summarization extraction method based on term vectors. The method includes the steps that S1, a deep neural network model is used for training linguistic data to obtain term vector representation of feature terms; S2, a sentence graph model is constructed; S3, the weights of sentences are calculated; S4, a maximum marginal relevance algorithm is used for generating a summarization. According to the method, a linguistic data set is collected and preprocessed to obtain a training feature linguistic data set, the deep neural network model is used for training the constructed training feature linguistic data set to obtain the term vectors of the feature terms, a candidate document set and a candidate sentence set are obtained from the linguistic data set through preset search terms, the semantic similarity between the senesces is obtained according to the term vectors of the feature terms, and then the semantic relation between every two sentences is obtained. The problem that in a traditional calculation method based on term co-occurrence, calculation errors are caused under the condition that semantic meaning is identical but terms are different is avoided, and therefore the accuracy of similarity calculation and the performance of the summarization are improved.

Description

technical field [0001] The invention relates to the fields of computer information retrieval and text mining, in particular to a method for automatically extracting document summaries based on word vectors. Background technique [0002] Text summarization technology is an important part of the text mining research field. This technology can find out the most important information in a document or document set and express it in a concise and coherent short text. With the advancement of science and technology and the development of network technology, there is a large amount of available information on the Internet. Faced with a large amount of data, this research can assist users to quickly understand the required information, save users' reading time, and improve work efficiency. [0003] The current text summarization technology is mainly extractive summarization, that is, extracting the most important sentences from the original text to form a summarization. The generation...

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Problems solved by technology

The value in the sentence similarity matrix represents the jump probability from a sentence to other sentences, so the calculation of node weights is very important, but when the traditional graph method calculates the similarity between sentences, it mostly uses the feature words contained in the sentence The co-occurrence is obtained, ignoring the semantic similarity between sentences, reducing the accuracy of node weight calculation, and affecting the performance of summarization

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