Adobe, Inc.

Vaishnavi Subramanian

Gaurush Hiranandani

Natwar Modani

Pranav Ravindra Maneriker

Atanu R Sinha

Shivani Gupta

Utpal

Multimedia document summarization techniques are described. That is, given a document that includes text and a set of images, various implementations generate a summary by extracting relevant text segments in the document and relevant segments of images with constraints on the amount of text and number/size of images in the summary.

Multimedia document summarization

A quality metric of a multimedia summary of a multimedia content item is determined based, in part, on semantic similarities of the summary and content item, rather than just on word frequencies. This is accomplished in some embodiments by identifying a semantic meaning of the summary and multimedia content item using vector analysis. The vectors of the summary and the vectors of the multimedia content item are compared to determine semantic similarity. In other examples, the quality metric of the multimedia summary is determined based on, in part, a coherence between an image portion of a summary and a text portion of the summary for determining a quality metric of a multimedia summary.

Determining quality of a summary of multimedia content

Growing community of inventors

Bengaluru, India

Vaishnavi Subramanian