公开(公告)号：US12164550B2

公开(公告)日：2024-12-10

申请号：US17651352

申请日：2022-02-16

Applicant: Adobe Inc.

Inventor： Sajad Sotudeh Gharebagh ,  Hanieh Deilamsalehy ,  Franck Dernoncourt

IPC: G06F40/30 ,  G06F16/33 ,  G06F16/34 ,  G06F18/21 ,  G06F18/22 ,  G06N3/04 ,  G06N3/08

Abstract: Techniques for training for and performing abstractive text summarization are disclosed. Such techniques include, in some embodiments, obtaining textual content, and generating a reconstruction of the textual content using a trained language model, the reconstructed textual content comprising an abstractive summary of the textual content generated based on relative importance parameters associated with respective portions of the textual content. In some cases, the trained language model includes a neural network language model that has been trained by identifying a plurality of discrete portions of training textual content, receiving the plurality of discrete portions of the training textual content as input to the language model, and predicting relative importance parameters associated with respective ones of the plurality of discrete portions of the training textual content, the relative importance parameters each being based at least on one or more linguistic similarity measures with respect to a ground truth.

公开(公告)号：US20230259544A1

公开(公告)日：2023-08-17

申请号：US17651352

申请日：2022-02-16

Applicant: Adobe Inc.

Inventor： Sajad Sotudeh Gharebagh ,  Hanieh Deilamsalehy ,  Franck Dernoncourt

IPC: G06F16/34 ,  G06K9/62 ,  G06N3/08 ,  G06N3/04 ,  G06F16/33

CPC classification number: G06F16/345 ,  G06K9/6215 ,  G06K9/6262 ,  G06N3/08 ,  G06N3/04 ,  G06F16/3334

Abstract: Techniques for training for and performing abstractive text summarization are disclosed. Such techniques include, in some embodiments, obtaining textual content, and generating a reconstruction of the textual content using a trained language model, the reconstructed textual content comprising an abstractive summary of the textual content generated based on relative importance parameters associated with respective portions of the textual content. In some cases, the trained language model includes a neural network language model that has been trained by identifying a plurality of discrete portions of training textual content, receiving the plurality of discrete portions of the training textual content as input to the language model, and predicting relative importance parameters associated with respective ones of the plurality of discrete portions of the training textual content, the relative importance parameters each being based at least on one or more linguistic similarity measures with respect to a ground truth.