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Evolutionary summarization generation method for internet news events

An Internet and news technology, applied in the direction of network data retrieval, website content management, unstructured text data retrieval, etc., can solve difficult problems such as user evolution and development process

Active Publication Date: 2018-02-13
SUZHOU UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The traditional multi-document summarization will take all the documents about hot events as a static document collection and generate summaries for them. The result of this is that it is difficult for users to understand the evolution of the event

Method used

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  • Evolutionary summarization generation method for internet news events
  • Evolutionary summarization generation method for internet news events
  • Evolutionary summarization generation method for internet news events

Examples

Experimental program
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Embodiment Construction

[0103] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, and are not intended to limit the present invention

[0104] refer to figure 1 , an evolutionary summary generation method for Internet news events, including:

[0105] S110. Input a collection of related news documents.

[0106] Store relevant documents of news events that need to generate evolutionary summaries in folders with corresponding dates according to their publication time, and store a document as a file, such as figure 2 As shown, the files in the folder of a certain date are as follows image 3 shown. The first line in each file is the news title, and the text after the first line, such as Figure 4 shown.

[0107] ...

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Abstract

The invention relates to an evolutionary summarization generation method for internet news events. The evolutionary summarization generation method includes the steps: inputting a related news document set; representing documents as topic facture vectors by an LDA (latent Dirichlet allocation) topic model; clustering the documents represented as the topic facture vectors; calculating local scoresof the documents in each topic; calculating global scores of the documents in each topic; calculating final scores of the documents in each topic; extracting document titles with high scores from eachtopic to serve as a summary according to time sequence; outputting the summary. The dimensions of the topic facture vectors are first preset values, and each cluster represents one topic. According to the evolutionary summarization generation method for the internet news events, the extracted summary has dynamic evolvability and is coherent and strong in readability, and experimental results indicate that the system is greatly improved in terms of redundancy, coherence and dynamic evolvability as compared with a traditional multi-document summarization system.

Description

technical field [0001] The invention relates to an abstract generation method, in particular to an evolutionary abstract generation method for Internet news events. Background technique [0002] Since evolutionary summarization for Internet news events is a new type of multi-document automatic summarization, the following not only introduces the research status of multi-document automatic summarization, but also introduces the research status related to the characteristics of evolutionary summarization, including: redundancy control and topic evolution. [0003] Research status of multi-document automatic summarization [0004] Multi-document Summarization (MDS) is a natural language processing technology that extracts the main information about multiple text descriptions under the same topic into a text summary according to the compression ratio (Radev et al. 2002). Usually multi-document automatic summarization includes three steps: text analysis, text content selection ...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06F17/27
CPCG06F16/35G06F16/986G06F40/258
Inventor 吴仁守王红玲
Owner SUZHOU UNIV
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