Text-to-Video Conversion via Semantic Analysis and Entity Extraction
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Solution Overview
Problem
Current methods for converting text-based content into video are time-consuming, costly, and fail to effectively summarize the main idea of the text, often resulting in videos that merely describe each sentence without conveying the overall essence.
Innovation Solution
A method that extracts and analyzes text data, including relevant sources, to summarize and visualize the content, defining movie characteristics, selecting entities and elements, creating a visualization tree, setting audio characteristics, and automatically assembling the video as a configuration file, allowing for quick and efficient conversion of text to video.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If text-to-video conversion is performed using existing methods, then video output is produced, but the process is time-consuming and costly
Solution Approach 1:
The system performs preliminary text analysis, summarization, and entity extraction before video generation. By pre-processing the text to identify key entities, relationships, and summary content, the actual video assembly process becomes much faster and more automated, reducing both time and cost
Solution Approach 2:
The system automatically analyzes text, extracts entities, generates summaries, and assembles videos without requiring manual human intervention at each step. The automated pipeline includes natural language processing, entity recognition, and video composition algorithms that operate autonomously to produce video output
2Loss of information
If existing text-to-video methods are used, then video is generated, but the main idea and essence of the text are not conveyed
Solution Approach 1:
The system extracts and separates the essential elements from the text including main entities, key relationships, and core summary content. By filtering out non-essential information and focusing only on the main idea and essential details, the video accurately conveys the essence of the source text
Solution Approach 2:
Different parts of the video are assigned different levels of detail and importance based on their relevance to the main idea. Key entities and summary content receive prominent visual representation, while less important details are minimized or omitted, ensuring the main essence is conveyed with high fidelity
3Extent of automation
If manual video creation processes are used, then high quality video is produced, but user interaction and complexity increase
Solution Approach 1:
The system implements a universal automated pipeline that handles multiple functions in sequence: text analysis, entity extraction, summary generation, and video assembly. This multi-functional automated system replaces multiple manual processes with a single integrated solution, increasing automation while managing complexity through modular design
Data Source
AI summary
According to the present invention there is provided a method for automatically converting text-based information and content to video form. In one embodiment of the invention the method creates a video which preserves the main idea of a given input text, and is adapted to convey the essence of the text. According to the invention data is extracted from the input text and from other sources of information relevant to it, so that the text can be analyzed as a whole and with respect to its main content. After extracting all the possible data, the text is semantically analyzed, summarized and converted to a video as a configuration file.