Text abstract intelligent extraction method and device, computer equipment and storage medium

Abstract

The invention provides a text abstract intelligent extraction method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining a plurality of feature statements from a plurality of texts, dividing feature words for each feature statement, and obtaining a plurality of feature words; classifying the plurality of feature words into different class clusters through clustering analysis; classifying the feature statement to which each feature word belongs into a corresponding class cluster; and extracting a fixed number of feature statements from each class cluster to form an overall abstract of the plurality of texts, wherein the clustering analysis process comprises the following steps: respectively carrying out word vector representation on the plurality of feature words to obtain a plurality of feature vectors; weighting each feature vector according to the importance degree to obtain a plurality of weighted vectors; calculating the similarity between every two weighting vectors; and performing clustering operation according to the similarity to obtain the number of clustering centers, and dividing the plurality of feature words into a plurality of class clusters according to the number of clustering centers.

Description

technical field [0001] The invention relates to the technical field of data mining, in particular to an intelligent extraction method, device, computer equipment and storage medium for a text summary. Background technique [0002] Automatic text summarization is a relatively difficult task in natural language processing. In essence, text summarization is a kind of information filtering. The output text is much less than the input text, but it contains the main information. According to the amount of text, text summarization can be divided into single-text summarization and multi-text summarization. The former is the basis of the latter, but the latter is not just a simple superposition of the results of the former. The former is often used to filter news information, while the latter has great potential in search engines, and the difficulty also increases accordingly. [0003] The summarization extracted by the traditional multi-text summarization algorithm has high redunda...

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Problems solved by technology

[0003] The summarization extracted by the traditional multi-text summarization algorithm has high redundancy and cannot reflect the overall structure and content of the entire text well. It is easy to cause the loss of the text topic center, low text topic coverage, poor coherence, and long time-consuming, etc. defect

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