公开(公告)号：US12130851B2

公开(公告)日：2024-10-29

申请号：US18209703

申请日：2023-06-14

Applicant: 42Maru Inc.

Inventor： Dong Hwan Kim ,  Han Su Kim ,  Woo Tae Jeong ,  Seung Hyeon Lee ,  Chang Hyeon Lim

IPC: G06F16/34 ,  G06F16/33 ,  G06F16/901 ,  G06F40/279 ,  G06F40/30 ,  G06F40/40

CPC classification number: G06F16/345 ,  G06F16/3347 ,  G06F16/9024 ,  G06F40/279 ,  G06F40/30 ,  G06F40/40

Abstract: The invention relates to a method and a system for improving performance of text summarization and has an object of improving performance of a technique for generating a summary from a given paragraph. According to the invention to achieve the object, a method for improving performance of text summarization includes: an a step of generating an embedding vector by vectorizing a natural language-based context; a b step of generating a graph using the embedding vector and calculating a first likelihood of each of at least one node included in the graph; a c step of generating a second likelihood by assigning a weight to the first likelihood according to a result of comparing at least one node included in the graph with the context; and a d step of calculating a third likelihood for all candidate paths present in the graph based on the second likelihood, selecting a path having a highest third likelihood, and generating a summary based on the path.

公开(公告)号：US11727041B2

公开(公告)日：2023-08-15

申请号：US17125991

申请日：2020-12-17

Applicant: 42Maru Inc.

Inventor： Dong Hwan Kim ,  Han Su Kim ,  Woo Tae Jeong ,  Seung Hyeon Lee ,  Chang Hyeon Lim

IPC: G06F40/279 ,  G06F40/40 ,  G06F40/30 ,  G06F16/34 ,  G06F16/33 ,  G06F16/901

CPC classification number: G06F16/345 ,  G06F16/3347 ,  G06F16/9024 ,  G06F40/279 ,  G06F40/30 ,  G06F40/40

Abstract: The invention relates to a method and a system for improving performance of text summarization and has an object of improving performance of a technique for generating a summary from a given paragraph. According to the invention to achieve the object, a method for improving performance of text summarization includes: an a step of generating an embedding vector by vectorizing a natural language-based context; a b step of generating a graph by using the embedding vector; a c step of assigning a weight depending on whether or not a keyword corresponding to at least one node included in the graph is present in the context; and a d step of selecting a path having a highest likelihood in the graph and generating a summary based on the path.