公开(公告)号：US11062087B2

公开(公告)日：2021-07-13

申请号：US16262655

申请日：2019-01-30

Applicant: Adobe Inc.

Inventor： Balaji Vasan Srinivasan ,  Kushal Chawla ,  Mithlesh Kumar ,  Hrituraj Singh ,  Arijit Pramanik

IPC: G06F40/284 ,  G06N20/00

Abstract: Certain embodiments involve tuning summaries of input text to a target characteristic using a word generation model. For example, a method for generating a tuned summary using a word generation model includes generating a learned subspace representation of input text and a target characteristic token associated with the input text by applying an encoder to the input text and the target characteristic token. The method also includes generating, by a decoder, each word of a tuned summary of the input text from the learned subspace representation and from a feedback about preceding words of the tuned summary. The tuned summary is tuned to target characteristics represented by the target characteristic token.

公开(公告)号：US11657225B2

公开(公告)日：2023-05-23

申请号：US17348257

申请日：2021-06-15

Applicant: Adobe Inc.

Inventor： Balaji Vasan Srinivasan ,  Kushal Chawla ,  Mithlesh Kumar ,  Hrituraj Singh ,  Arijit Pramanik

IPC: G06F40/284 ,  G06N20/00

CPC classification number: G06F40/284 ,  G06N20/00

Abstract: Systems and methods for generating a tuned summary using a word generation model. An example method includes receiving, at a decoder of the word generation model, a training data learned subspace representation of training data. The method also includes identifying tunable linguistic characteristics of the word generation model and training the decoder to output a training tuned summary of the training data learned subspace representation based on at least one of the tunable linguistic characteristics. The method further includes receiving an input text and a target characteristic token, and generating, by the trained decoder of the word generation model, each word of a tuned summary of the input text from a learned subspace representation and from feedback about preceding words of the tuned summary, wherein the tuned summary is tuned to target characteristics represented by the target characteristic token.

公开(公告)号：US20210312129A1

公开(公告)日：2021-10-07

申请号：US17348257

申请日：2021-06-15

Applicant: Adobe Inc.

Inventor： Balaji Vasan Srinivasan ,  Kushal Chawla ,  Mithlesh Kumar ,  Hrituraj Singh ,  Arijit Pramanik

IPC: G06F40/284 ,  G06N20/00

Abstract: Certain embodiments involve tuning summaries of input text to a target characteristic using a word generation model. For example, a method for generating a tuned summary using a word generation model includes generating a learned subspace representation of input text and a target characteristic token associated with the input text by applying an encoder to the input text and the target characteristic token. The method also includes generating, by a decoder, each word of a tuned summary of the input text from the learned subspace representation and from a feedback about preceding words of the tuned summary. The tuned summary is tuned to target characteristics represented by the target characteristic token.

公开(公告)号：US20200242197A1

公开(公告)日：2020-07-30

申请号：US16262655

申请日：2019-01-30

Applicant: Adobe Inc.

Inventor： Balaji Vasan Srinivasan ,  Kushal Chawla ,  Mithlesh Kumar ,  Hrituraj Singh ,  Arijit Pramanik

IPC: G06F17/27 ,  G06N20/00

Abstract: Certain embodiments involve tuning summaries of input text to a target characteristic using a word generation model. For example, a method for generating a tuned summary using a word generation model includes generating a learned subspace representation of input text and a target characteristic token associated with the input text by applying an encoder to the input text and the target characteristic token. The method also includes generating, by a decoder, each word of a tuned summary of the input text from the learned subspace representation and from a feedback about preceding words of the tuned summary. The tuned summary is tuned to target characteristics represented by the target characteristic token.