公开(公告)号：US20250005063A1

公开(公告)日：2025-01-02

申请号：US18344739

申请日：2023-06-29

Applicant: Amazon Technologies, Inc.

Inventor： Devang Kulshreshtha ,  Saket Dingliwal ,  Sravan Babu Bodapati ,  Katrin Kirchhoff ,  Sarthak Handa

IPC: G06F16/34 ,  G06F40/169 ,  G06F40/40

Abstract: Pairs of text collections are obtained. An individual pair comprises (a) a source text collection which includes a first group of text sequences and (b) an annotated analysis result of the source text collection, comprising a second group of text sequences and a set of evidence mappings generated by an evidence mapping model. An evidence mapping indicates, for a particular text sequence of the second group, another text sequence of the first group which provides evidence for the particular text sequence. A quality metric of the model is obtained using an automated evaluation methodology in which a question is generated from the particular text sequence, and an analysis of a pair of answers (including 10 an answer generated using an evidence mapping) to the question is performed. The quality metric is provided via a programmatic interface.

公开(公告)号：US20240428002A1

公开(公告)日：2024-12-26

申请号：US18339749

申请日：2023-06-22

Applicant: Amazon Technologies, Inc.

Inventor： Aparna Elangovan ,  Lei Xu ,  Devang Kulshreshtha ,  Sravan Babu Bodapati ,  Katrin Kirchhoff ,  Sarthak Handa

IPC: G06F40/35 ,  G06F40/40

Abstract: A medical audio summarization service receives a medical conversation and an indication of a user preferred summarization style selected from a plurality of available summarization styles to generate a medical summary that conforms to the user preferred summarization style. A transcript is generated via a medical audio transcription service, and the transcript is used by a natural language processing engine (including a large language model) to generate the medical summary. The large language model is trained to be used to generate medical summaries that conform to respective ones of a plurality of user preferred summarization styles. The large language model is trained using training data comprising previously generated summaries and summary interaction metadata generated from user edits and/or feedback.