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    Generating a topic-based summary of textual content
    1.
    发明授权
    Generating a topic-based summary of textual content 有权

    公开(公告)号：US10685050B2

    公开(公告)日：2020-06-16

    申请号：US15960505

    申请日：2018-04-23

    Applicant: Adobe Inc.

    Inventor： Kundan Krishna ,  Balaji Vasan Srinivasan

    IPC: G06F16/34 ,  G06F16/33 ,  G06N20/00 ,  G06F16/31

    Abstract: 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.

    Visualizing natural language through 3D scenes in augmented reality
    2.
    发明授权
    Visualizing natural language through 3D scenes in augmented reality 有权

    公开(公告)号：US10665030B1

    公开(公告)日：2020-05-26

    申请号：US16247235

    申请日：2019-01-14

    Applicant: Adobe Inc.

    Inventor： Sumit Shekhar ,  Paridhi Maheshwari ,  Monisha J ,  Kundan Krishna ,  Amrit Singhal ,  Kush Kumar Singh

    IPC: G06T19/00 ,  G06F40/40

    Abstract: 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.

    Generating a Targeted Summary of Textual Content Tuned to a Target Audience Vocabulary
    3.
    发明申请
    Generating a Targeted Summary of Textual Content Tuned to a Target Audience Vocabulary 审中-公开

    公开(公告)号：US20190266228A1

    公开(公告)日：2019-08-29

    申请号：US16407596

    申请日：2019-05-09

    Applicant: Adobe Inc.

    Inventor： Saumitra Sharma ,  Kundan Krishna ,  Balaji Vasan Srinivasan ,  Aniket Murhekar

    IPC: G06F17/22 ,  G06N20/00 ,  G06F17/27

    Abstract: 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 Topic-Based Summary of Textual Content
    5.
    发明申请
    Generating a Topic-Based Summary of Textual Content 审中-公开

    公开(公告)号：US20190325066A1

    公开(公告)日：2019-10-24

    申请号：US15960505

    申请日：2018-04-23

    Applicant: Adobe Inc.

    Inventor： Kundan Krishna ,  Balaji Vasan Srinivasan

    IPC: G06F17/30 ,  G06F15/18

    Abstract: 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.

    CONSTRUCTING CONTENT BASED ON MULTI-SENTENCE COMPRESSION OF SOURCE CONTENT
    6.
    发明申请
    CONSTRUCTING CONTENT BASED ON MULTI-SENTENCE COMPRESSION OF SOURCE CONTENT 审中-公开

    公开(公告)号：US20190197184A1

    公开(公告)日：2019-06-27

    申请号：US15854320

    申请日：2017-12-26

    Applicant: ADOBE INC.

    Inventor： Balaji Vasan Srinivasan ,  Pranav Ravindra Maneriker ,  Natwar Modani ,  Kundan Krishna

    IPC: G06F17/30 ,  G06F17/27

    CPC classification number: G06F16/334 ,  G06F16/338 ,  G06F17/2705 ,  G06F17/277

    Abstract: 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.

    Generating a targeted summary of textual content tuned to a target audience vocabulary
    9.
    发明授权
    Generating a targeted summary of textual content tuned to a target audience vocabulary 有权

    公开(公告)号：US10534854B2

    公开(公告)日：2020-01-14

    申请号：US16407596

    申请日：2019-05-09

    Applicant: Adobe Inc.

    Inventor： Saumitra Sharma ,  Kundan Krishna ,  Balaji Vasan Srinivasan ,  Aniket Murhekar

    IPC: G06F17/22 ,  G06F17/27 ,  G06N20/00

    Abstract: 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
    10.
    发明授权
    Generating a targeted summary of textual content tuned to a target audience vocabulary 有权

    公开(公告)号：US10409898B2

    公开(公告)日：2019-09-10

    申请号：US15816976

    申请日：2017-11-17

    Applicant: Adobe Inc.

    Inventor： Saumitra Sharma ,  Kundan Krishna ,  Balaji Vasan Srinivasan ,  Aniket Murhekar

    IPC: G06F17/22 ,  G06F17/27 ,  G06N20/00

    Abstract: 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.

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