US 11,657,225 B2
Generating summary content tuned to a target characteristic using a word generation model
Balaji Vasan Srinivasan, Bangalore (IN); Kushal Chawla, Bengaluru (IN); Mithlesh Kumar, Gaya (IN); Hrituraj Singh, Amroha (IN); and Arijit Pramanik, Kolkata (IN)
Assigned to ADOBE INC., San Jose, CA (US)
Filed by Adobe Inc., San Jose, CA (US)
Filed on Jun. 15, 2021, as Appl. No. 17/348,257.
Application 17/348,257 is a continuation of application No. 16/262,655, filed on Jan. 30, 2019, granted, now 11,062,087.
Prior Publication US 2021/0312129 A1, Oct. 7, 2021
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 40/284 (2020.01); G06N 20/00 (2019.01)
CPC G06F 40/284 (2020.01) [G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A method for generating a tuned summary using a word generation model, wherein the method includes one or more processing devices performing operations comprising:
receiving, at a decoder of the word generation model, a training data learned subspace representation of training data;
identifying tunable linguistic characteristics of the word generation model;
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;
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 a feedback about preceding words of the tuned summary, wherein the tuned summary is tuned to target characteristics represented by the target characteristic token.