Georgia State University Research Foundation, Inc. (20240267601). INTELLIGENT AUTOMATED CONTENT SUMMARY GENERATION simplified abstract
Contents
INTELLIGENT AUTOMATED CONTENT SUMMARY GENERATION
Organization Name
Georgia State University Research Foundation, Inc.
Inventor(s)
Anthony Lemieux of Decatur GA (US)
Krishanu Sarker of Marietta GA (US)
INTELLIGENT AUTOMATED CONTENT SUMMARY GENERATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240267601 titled 'INTELLIGENT AUTOMATED CONTENT SUMMARY GENERATION
The abstract describes a content summarizing program that processes audio and/or visual content of a media item to determine segments and their rankings, selects summary segments based on time criteria, generates segment transitions, and stitches together summary segments to create a content summary.
- The program analyzes media content to identify representative segments and ranks them accordingly.
- Summary segments are selected based on rankings and time criteria to create a cohesive content summary.
- Segment transitions are generated to smoothly connect adjacent summary segments.
- The program stitches together summary segments with transitions to form a complete content summary.
Potential Applications: - Automated video editing for content creators - Enhancing user experience in media consumption platforms - Improving searchability and accessibility of multimedia content
Problems Solved: - Streamlining the process of creating content summaries - Enhancing the efficiency of segment selection and transition generation - Providing a more engaging and concise way to consume media content
Benefits: - Time-saving for content creators and editors - Improved user engagement and retention - Enhanced accessibility for individuals with limited time for media consumption
Commercial Applications: Title: Automated Content Summarization Tool for Media Production This technology can be utilized in video editing software, streaming platforms, and online media repositories to streamline content summarization processes, improve user experience, and increase viewer engagement.
Prior Art: Researchers in the field of multimedia content analysis and summarization have explored various techniques for segmenting and summarizing media content. Previous studies have focused on audio-visual processing algorithms and machine learning models to automate content summarization tasks.
Frequently Updated Research: Ongoing research in the field of multimedia content analysis continues to explore advancements in automated summarization techniques, including the integration of artificial intelligence and natural language processing algorithms for more accurate and efficient content summarization.
Questions about Content Summarization Technology: 1. How does the program determine the ranking of segments in the media content? 2. What are the key factors considered when selecting summary segments based on time criteria?
Original Abstract Submitted
a content summarizing program processes audio and/or visual content of a media item to determine a plurality of segments of the content; and determines a respective ranking for each of the plurality of segments. the respective ranking indicates how representative of the content as a whole the segment is. based on the plurality of respective rankings and summary time criteria, the content summarizing program selects summary segments from the plurality of segments; and, based on the summary segments, generates a segment transition. the segment transition corresponds to a pair of adjacent summary segments and comprises characteristics of both first and second summary segments of the pair of adjacent summary segments. the content summarizing program stitches together the summary segments using the segment transition to generate a content summary. the summary segments are stitched together with the segment transition disposed between the first summary segment and the second summary segment.
