Video-Based Text Material Generation Using Popularity Ranking
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Solution Overview
Problem
Existing short video recording processes face challenges in creating high-quality text materials that can attract audiences, necessitating improved methods for obtaining and generating relevant information.
Innovation Solution
A method and apparatus for obtaining text material by selecting high-quality candidate videos based on popularity and object information, generating text material from these videos, and displaying recommended videos to users for creative inspiration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multiple candidate videos are collected and manually evaluated to create text material, then the quality of text material can be improved, but the time consumption and operational complexity increase significantly
Solution Approach 1:
The system enables automatic self-service by using AI algorithms to automatically evaluate candidate videos based on popularity metrics (view count, likes, comments) and generate text material without human intervention. The processor automatically identifies high-quality videos and extracts key information to create text content, eliminating the need for manual video evaluation and text creation while maintaining high quality standards
Solution Approach 2:
The patent replaces the mechanical manual process of video evaluation and text creation with an automated information processing system. The processor uses algorithmic evaluation based on playing data and object information to substitute human judgment, and employs automated text generation techniques to replace manual text writing, thereby dramatically reducing time consumption while preserving quality
2Reliability
If comprehensive video evaluation criteria are used to ensure quality selection, then the reliability of recommended videos improves, but the complexity of the selection process increases
Solution Approach 1:
The video evaluation process is segmented into distinct analytical dimensions: popularity evaluation (based on view count, likes, comments) and object information evaluation (based on relevance to target object). This segmentation allows the system to maintain comprehensive evaluation criteria while simplifying the implementation by breaking down the complex selection process into manageable, independent evaluation modules that can be processed algorithmically
3Productivity
If automated text generation from videos is implemented, then productivity improves, but the precision and quality of generated text material may deteriorate
Solution Approach 1:
The system introduces an intermediary processing layer between video content and final text material. This intermediary involves extracting key information from selected videos through automated analysis, then synthesizing this extracted information into coherent text material. The intermediary process ensures that while automation improves productivity, the generated text maintains quality by being based on systematically extracted key information rather than random generation
Data Source
AI summary
Embodiments of the present disclosure relate to a method, apparatus, device, storage medium, and program product for obtaining text material, comprising: in response to a material obtaining instruction, obtaining a set of candidate videos associated with a target object comprising a plurality of posted candidate videos; for each candidate video, determining the popularity of the candidate video based on the playing data of the candidate video and/or the object information of the explained object included in the candidate video; selecting recommended videos based on popularity for display; in response to a selection operation for the recommended video, generating text material corresponding to the target object based on key information in the recommended video. In this way, users can quickly review the recommended videos and obtain creative inspiration.