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    Augmenting text with multimedia assets
    1.
    发明授权
    Augmenting text with multimedia assets 有权

    公开(公告)号：US11822868B2

    公开(公告)日：2023-11-21

    申请号：US15906388

    申请日：2018-02-27

    Applicant: Adobe Inc.

    Inventor： Emre Demiralp ,  Gavin Stuart Peter Miller ,  Walter W. Chang ,  Grayson Squier Lang ,  Daicho Ito

    IPC: G06F16/00 ,  G06F40/134 ,  G06F40/279

    CPC classification number: G06F40/134 ,  G06F16/00 ,  G06F40/279

    Abstract: Systems and methods are provided for providing a navigation interface to access or otherwise use electronic content items. In one embodiment, an augmentation application identifies at least one entity referenced in a document. The entity can be referenced in at least two portions of the document by at least two different words or phrases. The augmentation application associates the at least one entity with at least one multimedia asset. The augmentation application generates a layout including at least some content of the document referencing the at least one entity and the at least one multimedia asset associated with the at least one entity. The augmentation application renders the layout for display.

    AUTOMATIC TEXT SEGMENTATION BASED ON RELEVANT CONTEXT
    3.
    发明申请
    AUTOMATIC TEXT SEGMENTATION BASED ON RELEVANT CONTEXT 审中-公开

    公开(公告)号：US20200311207A1

    公开(公告)日：2020-10-01

    申请号：US16368334

    申请日：2019-03-28

    Applicant: ADOBE INC.

    Inventor： Seokhwan Kim ,  Walter W. Chang ,  Nedim Lipka ,  Franck Dernoncourt ,  Chan Young Park

    IPC: G06F17/27 ,  G06N3/08

    Abstract: Methods and systems are provided for identifying subparts of a text. A neural network system can receive a set of sentences that includes context sentences and target sentences that indicate a decision point in a text. The neural network system can generate context vector sentences and target sentence vectors by encoding context from the set of sentences. These context sentence vectors can be weighted to focus on relevant information. The weighted context sentence vectors and the target sentence vectors can then be used to output a label for the decision point in the text.

    ABSTRACTIVE SUMMARIZATION OF LONG DOCUMENTS USING DEEP LEARNING
    6.
    发明申请
    ABSTRACTIVE SUMMARIZATION OF LONG DOCUMENTS USING DEEP LEARNING 审中-公开

    公开(公告)号：US20190278835A1

    公开(公告)日：2019-09-12

    申请号：US15915775

    申请日：2018-03-08

    Applicant: Adobe Inc.

    Inventor： Arman Cohan ,  Walter W. Chang ,  Trung Huu Bui ,  Franck Dernoncourt ,  Doo Soon Kim

    IPC: G06F17/22 ,  G06N3/04 ,  G06F17/30 ,  G06F17/27

    Abstract: Techniques are disclosed for abstractive summarization process for summarizing documents, including long documents. A document is encoded using an encoder-decoder architecture with attentive decoding. In particular, an encoder for modeling documents generates both word-level and section-level representations of a document. A discourse-aware decoder then captures the information flow from all discourse sections of a document. In order to extend the robustness of the generated summarization, a neural attention mechanism considers both word-level as well as section-level representations of a document. The neural attention mechanism may utilize a set of weights that are applied to the word-level representations and section-level representations.

    Multitask Machine-Learning Model Training and Training Data Augmentation
    8.
    发明公开
    Multitask Machine-Learning Model Training and Training Data Augmentation 审中-公开

    公开(公告)号：US20230419164A1

    公开(公告)日：2023-12-28

    申请号：US17846428

    申请日：2022-06-22

    Applicant: Adobe Inc.

    Inventor： Khalil Mrini ,  Franck Dernoncourt ,  Seunghyun Yoon ,  Trung Huu Bui ,  Walter W. Chang ,  Emilia Farcas ,  Ndapandula T. Nakashole

    IPC: G06N20/00

    CPC classification number: G06N20/00

    Abstract: Multitask machine-learning model training and training data augmentation techniques are described. In one example, training is performed for multiple tasks simultaneously as part of training a multitask machine-learning model using question pairs. Examples of the multiple tasks include question summarization and recognizing question entailment. Further, a loss function is described that incorporates a parameter sharing loss that is configured to adjust an amount that parameters are shared between corresponding layers trained for the first and second tasks, respectively. In an implementation, training data augmentation techniques are also employed by synthesizing question pairs, automatically and without user intervention, to improve accuracy in model training.

    Automatic text segmentation based on relevant context
    9.
    发明授权
    Automatic text segmentation based on relevant context 有权

    公开(公告)号：US11210470B2

    公开(公告)日：2021-12-28

    申请号：US16368334

    申请日：2019-03-28

    Applicant: ADOBE INC.

    Inventor： Seokhwan Kim ,  Walter W. Chang ,  Nedim Lipka ,  Franck Dernoncourt ,  Chan Young Park

    IPC: G06F40/30 ,  G06N3/08

    Abstract: Methods and systems are provided for identifying subparts of a text. A neural network system can receive a set of sentences that includes context sentences and target sentences that indicate a decision point in a text. The neural network system can generate context vector sentences and target sentence vectors by encoding context from the set of sentences. These context sentence vectors can be weighted to focus on relevant information. The weighted context sentence vectors and the target sentence vectors can then be used to output a label for the decision point in the text.

    Natural language embellishment generation and summarization for question-answering systems
    10.
    发明授权
    Natural language embellishment generation and summarization for question-answering systems 有权

    公开(公告)号：US10909111B2

    公开(公告)日：2021-02-02

    申请号：US14971180

    申请日：2015-12-16

    Applicant: ADOBE INC.

    Inventor： Walter W. Chang ,  Elahe Rahimtoroghi

    IPC: G06F16/00 ,  G06F16/242

    Abstract: Systems and methods are disclosed for augmenting or summarizing an information processing task in a bilateral Q&A format. An initial query is received from an analyst user to conduct an analysis on user-provided data attributes. A primary result is generated from processing the initial query. In addition, defined data attributes associated with the primary result are ranked to create relevant follow-up queries. A summary of the primary result, using both graphical representations and natural language summaries, are provided to the analyst user. The relevant follow-up queries can also be provided to the analyst user, thereby progressing a contextually-based conversation regarding the data. The analytics session can progress as the user traverses the results and follow-up queries, until the user terminates the session or all relevant follow-up queries are exhausted. A concise narrative of the session with varying levels of detail specified by the user is presented in natural language to provide the analyst user with a relevant summary of the performed analysis.

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