Adobe, Inc.

Kundan Krishna

Balaji Vasan Srinivasan

Pranav Ravindra Maneriker

Saumitra Sharma

Aniket Murhekar

Shiv Kumar Saini

Sumit Shekhar

Natwar Modani

Tanya Goyal

Anandhavelu Natarajan

Paridhi Maheshwari

Cedric Huesler

Monisha J

Amrit Singhal

Kush Kumar Singh

Embodiments of the present invention provide systems, methods, and computer storage media directed to facilitating corpus-based content generation, in particular, using graph-based multi-sentence compression to generate a final content output. In one embodiment, pre-existing source content is identified and retrieved from a corpus. The source content is then parsed into sentence tokens, mapped and weighted. The sentence tokens are further parsed into word tokens and weighted. The mapped word tokens are then compressed into candidate sentences to be used in a final content. The final content is assembled using ranked candidate sentences, such that the final content is organized to reduce information redundancy and optimize content cohesion.

Constructing content based on multi-sentence compression of source content

Embodiments are disclosed for bundling and arranging online content fragments for presentation based on content-specific metrics and inter-content constraints. For example, a content management application accesses candidate content fragments, a content-specific metric, and an inter-content constraint. The content management application computes minimum and maximum contribution values for the candidate content fragments. The content management application selects, based on the computed minimum and maximum contribution values, a subset of the candidate content fragments. The content management application applies, subject to the inter-content constraint, a bundle-selection function to the selected candidate content fragments and thereby identifies a bundle of online content fragments. The content management application outputs the identified bundle of online content fragments for presentation via an online service.

Bundling online content fragments for presentation based on content-specific metrics and inter-content constraints

A word generation model obtains textual content and a requested topic of interest, and generates a targeted summary of the textual content tuned to the topic of interest. To do so, a topic-aware encoding model encodes the textual content with a topic label corresponding to the topic of interest to generate topic-aware encoded text. A word generation model selects a next word for the topic-based summary from the topic-aware encoded text. The word generation model is trained to generate topic-based summaries using machine learning on training data including a multitude of documents, a respective summary of each document, and a respective topic of each summary. Feedback of the selected next word is provided to the word generation model. The feedback causes the word generation model to select subsequent words for the topic-based summary based on the feedback of the next selected word.

Generating a topic-based summary of textual content

A natural language scene description is converted into a scene that is rendered in three dimensions by an augmented reality (AR) display device. Text-to-AR scene conversion allows a user to create an AR scene visualization through natural language text inputs that are easily created and well-understood by the user. The user can, for instance, select a pre-defined natural language description of a scene or manually enter a custom natural language description. The user can also select a physical real-world surface on which the AR scene is to be rendered. The AR scene is then rendered using the augmented reality display device according to its natural language description using 3D models of objects and humanoid characters with associated animations of those characters, as well as from extensive language-to-visual datasets. Using the display device, the user can move around the real-world environment and experience the AR scene from different angles.

Visualizing natural language through 3D scenes in augmented reality

A targeted summary of textual content tuned to a target audience vocabulary is generated in a digital medium environment. A word generation model obtains textual content, and generates a targeted summary of the textual content. During the generation of the targeted summary, the words of the targeted summary generated by the word generation model are tuned to the target audience vocabulary using a linguistic preference model. The linguistic preference model is trained, using machine learning on target audience training data corresponding to a corpus of text of the target audience vocabulary, to learn word preferences of the target audience vocabulary between similar words (e.g., synonyms). After each word is generated using the word generation model and the linguistic preference model, feedback regarding the generated word is provided back to the word generation model. The feedback is utilized by the word generation model to generate subsequent words of the summary.

Generating a targeted summary of textual content tuned to a target audience vocabulary

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Bengaluru, India

Kundan Krishna