公开(公告)号：US20220179893A1

公开(公告)日：2022-06-09

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

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.

公开(公告)号：US20250021590A1

公开(公告)日：2025-01-16

申请号：US18898839

申请日：2024-09-27

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

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: calculating a first likelihood of each of a plurality of nodes included in a graph corresponding to a natural language-based context; calculating a second likelihood of each of the plurality of nodes by assigning a weight to a first likelihood of a node corresponding to a keyword not presenting in the context among a plurality of keywords corresponding to each of the plurality of nodes; calculating a third likelihood of each of all paths present in the graph based on the second likelihood of each of the plurality of nodes; and generating a summary for the context based on a path having the highest third likelihood among the paths.