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Case element and BiGRU-based case-related public opinion news text abstract method

A technology of cases and elements, applied in the field of natural language processing, to achieve the effect of improving the F value

Active Publication Date: 2019-11-22
KUNMING UNIV OF SCI & TECH
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

General text summarization methods usually focus on open-domain text summarization tasks, and cannot make good use of domain knowledge to guide the summarization process, while public opinion news texts involved in cases usually have topic information related to specific cases, which can be guided by some case elements. A summary closer to the topic of the news text in the field involved

Method used

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  • Case element and BiGRU-based case-related public opinion news text abstract method
  • Case element and BiGRU-based case-related public opinion news text abstract method
  • Case element and BiGRU-based case-related public opinion news text abstract method

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Embodiment 1

[0063] Embodiment 1: as Figure 1-2 As shown, based on the case elements and BiGRU's public opinion news text summary method, the specific steps of the case-related public opinion news text summary method based on the case elements and BiGRU are as follows:

[0064] Step1. Acquisition of public opinion news texts involved in the case; use crawlers based on the Scrapy framework to collect relevant news from the Internet, and perform manual proofreading to construct a text dataset of public opinion news texts involved in the case. The relevant information of the dataset is shown in Table 2:

[0065] Table 2 Dataset

[0066] number of texts average number of sentences average text length Training set 17434 15.38 776 validation set 1000 16.46 776 test set 1000 14.42 722

[0067] Step2, mark the training data;

[0068] A greedy search method is used to find a sentence combination with the highest score as a summary sentence through the...

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Abstract

The invention relates to a case element and BiGRU-based case-related public opinion news text abstract method, and belongs to the technical field of natural language processing. According to the method, firstly, a case-related public opinion news abstract data set is constructed, related case elements are defined, then case element information is fused into the word and sentence double-layer coding process of news texts through an attention mechanism, news text representation with the case element information is generated, finally, sentences are classified through a multi-feature classification layer, and abstracts are obtained. According to the method, the important information is obtained from the public opinion news text related to the specific case to serve as the brief abstract. And the method plays an important role in quickly mastering the public opinion situation by related personnel.

Description

technical field [0001] The invention relates to a method for summarizing case-related public opinion news texts based on case elements and BiGRU, and belongs to the technical field of natural language processing. Background technique [0002] Case-related public opinion refers to Internet public opinion related to judicial cases. Compared with general news public opinion, the public opinion involved in the case is sensitive and specific, which can easily induce mass incidents and cause serious adverse social effects. Therefore, it is very important to quickly and accurately obtain public opinion information and control the development trend of public opinion to prevent and resolve public opinion risks. The text summarization task can simplify the length of the news text by extracting core sentences that can summarize the semantics of the text, which can effectively help users obtain key information about public opinion events from a large amount of public opinion data. Gen...

Claims

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Application Information

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IPC IPC(8): G06F16/34G06F16/35G06F16/951
CPCG06F16/345G06F16/35G06F16/951
Inventor 余正涛韩鹏宇黄于欣高盛祥郭军军王振晗
Owner KUNMING UNIV OF SCI & TECH
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