Adobe, Inc.

Nikitha Srikanth

Nikaash Puri

Balaji Krishnamurthy

Mihir Naware

Milan Aggarwal

Sumit Bhatia

Yaman Kumar

Hyman Chung

Simra Shahid

Matthew William Rozen

Tanay Anand

Jayakumar Subramanian

Pranjal Prasoon

Somesh Singh

Ganesh Karbhari Palwe

Nithyakala Sainath

Nisarg Shailesh Joshi

Seoyoung Park

In implementations of systems for digital content analysis, a computing device implements an analysis system to extract a first content component and a second content component from digital content to be analyzed based on content metrics. The analysis system generates first embeddings using a first machine learning model and second embedding using a second machine learning model. The first embeddings and the second embeddings are combined as concatenated embeddings. The analysis system generates an indication of a content metric for display in a user interface using a third machine learning model based on the concatenated embeddings.

Digital content analysis

Some techniques described herein relate to generating a hierarchical topic model (HTM), which can be used to generate custom content. In one example, a method includes determining first-level topics in a topic hierarchy related to a corpus of documents. A first-level topic of the first-level topics includes multiple words. The multiple words are grouped into clusters based on word embeddings of the multiple words. The multiple words are then subdivided into second-level topics as subtopics of the first-level topic, such that the number of second-level topics equals the number of clusters. A document of the corpus of documents is assigned to the first-level topic and to a second-level topic of the second-level topics, and an indication is received of access by a user to the document. Custom content is generated for the user based on one or more other documents assigned to the first-level topic and the second-level topic.

Hierarchical topic model with an interpretable topic hierarchy

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Mysore, India

Nikitha Srikanth