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US Patent 11494564 Unsupervised aspect-based multi-document abstractive summarization

Patent 11494564 was granted and assigned to NAVER (company) on November, 2022 by the United States Patent and Trademark Office.

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Is a
Patent
Patent

Patent attributes

Patent Applicant
NAVER (company)
NAVER (company)
Current Assignee
NAVER (company)
NAVER (company)
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
11494564
Date of Patent
November 8, 2022
Patent Application Number
16832841
Date Filed
March 27, 2020
Patent Citations
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US Patent 10402723 Multi-stage machine-learning models to control path-dependent processes
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US Patent 10776566 System and method of document generation
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US Patent 10839223 System and method for localization of activities in videos
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US Patent 11270085 Generating method, generating device, and recording medium
Patent Citations Received
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US Patent 11636847 Ontology-augmented interface
0
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US Patent 11854531 Cross-class ontology integration for language modeling
0
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US Patent 11983210 Methods and systems for generating summaries given documents with questions and answers
0
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US Patent 11790889 Feature engineering with question generation
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US Patent 11699432 Cross-context natural language model generation
0
Patent Primary Examiner
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David Phantana-angkool
CPC Code
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G06N 3/0445
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G06N 3/0454
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G06F 16/953
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G06F 16/93
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G06F 40/30
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G06F 40/166
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G06N 3/08

A multi-document summarization system includes: an encoding module configured to receive multiple documents associated with a subject and to, using a first model, generate vector representations for sentences, respectively, of the documents; a grouping module configured to group first and second ones of the sentences associated with first and second aspects into first and second groups, respectively; a group representation module configured to generate a first vector representation based on the first ones of the sentences and a second vector representation based on the second ones of the sentences; a summary module configured to: using a second model: generate a first sentence regarding the first aspect based on the first vector representation; and generate a second sentence regarding the second aspect based on the second vector representation; and store a summary including the first and second sentences in memory in association with the subject.

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