News Topic Title Generation via Probabilistic Keyword Extraction
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Solution Overview
Problem
Existing methods for classifying news events into topics require manual redefinition of keyword bags, which are disordered and inefficient for user readability.
Innovation Solution
An automated method using an electronic device that applies a topic model, such as an implicit Dirichlet distribution topic model, to analyze news event text, select keywords within a preset probability distribution range, and determine time intervals, thereby generating a reduced word bag and calculating text similarities to create readable topic titles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual redefinition of topic keywords is performed, then readability of news event topics is improved, but time consumption and efficiency deteriorate
Solution Approach 1:
The system automatically performs topic keyword extraction and organization using unsupervised learning algorithms, enabling the topic modeling process to serve itself without requiring manual intervention for keyword redefinition, thus resolving the contradiction between readability improvement and time consumption
Solution Approach 2:
The patent replaces the manual mechanical process of keyword redefinition with an automated computational system using topic modeling algorithms, substituting human labor with machine-based automatic keyword extraction and organization to improve efficiency while maintaining readability
2Loss of information
If complete word bag of keywords is retained, then information completeness is improved, but user readability and processing efficiency deteriorate
Solution Approach 1:
The system extracts only the most representative and relevant keywords from the complete word bag based on probability distributions and topic modeling results, separating essential information from redundant data to improve readability while preserving core information content
Solution Approach 2:
The patent applies different processing qualities to different parts of the keyword bag by selectively emphasizing high-probability keywords and de-emphasizing low-probability ones, creating a non-uniform keyword structure that optimizes both information retention and readability through localized keyword selection
Data Source
AI summary
A method for automatically generating news event of a certain topic applied in an electronic device analyzes text of the news event by a topic model to obtain topics, a probability distribution of keywords in each topic is established, and a time interval distribution of the keywords in each topic is calculated. Keywords within a preset probability distribution range are selected to reduce the size of a word bag relating to the topic, and a time interval range of the reduced word bag of the topic is determined. A calculation of text similarities of the text in a database is made to obtain a news article corresponding to each topic according to the time interval range of the reduced word bag, and a title of the news article as a target topic of the text of the news event is determined.