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1.发明申请公开(公告)号：US20190155877A1
公开(公告)日：2019-05-23
申请号：US15816976
申请日：2017-11-17
Applicant: Adobe Inc.
Inventor： Saumitra Sharma , Kundan Krishna , Balaji Vasan Srinivasan , Aniket Murhekar
CPC classification number: G06F17/2264 , G06F16/345 , G06F17/274 , G06F17/2785 , G06F17/2795 , G06F17/2854 , G06N3/0445 , G06N3/0454 , G06N3/08 , 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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2.发明申请公开(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
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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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
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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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
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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