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Fine-Tune Whisper and Transformer Large Language Model for Meeting Summarization.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Fine-Tune Whisper and Transformer Large Language Model for Meeting Summarization./
作者:	Ge, Fei.
面頁冊數:	1 online resource (41 pages)
附註:	Source: Masters Abstracts International, Volume: 85-12.
Contained By:	Masters Abstracts International85-12.
標題:	Statistics. -
電子資源:	click for full text (PQDT)
ISBN:	9798382822785

Fine-Tune Whisper and Transformer Large Language Model for Meeting Summarization.
Ge, Fei.

Fine-Tune Whisper and Transformer Large Language Model for Meeting Summarization. - 1 online resource (41 pages)

Source: Masters Abstracts International, Volume: 85-12.

Thesis (M.S.)--University of California, Los Angeles, 2024.

Includes bibliographical references

With globalization escalating, multinational companies frequently hold meetings involving both domestic and international employees. However, time zone differences often result in international employees missing some meetings. This thesis explores an innovative solution to address this issue and ensure that colleagues who miss meetings can quickly catch up on the content. The core of this solution involves fine-tuning the Whisper model to convert audio recordings of meetings to text, followed by advanced summary transformers based on fine-tuning Llama3 and specific prompts to summarize the converted text. The resulting summaries provide a concise and comprehensive overview of the meeting's content, which can then be distributed to employees who could not attend due to time zone constraints. This approach not only enhances the efficiency of work communication among colleagues but also optimizes the global management and operational efficiency of the company.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382822785Subjects--Topical Terms:

556824
Statistics.
Subjects--Index Terms:

International employeesIndex Terms--Genre/Form:

554714
Electronic books.

Fine-Tune Whisper and Transformer Large Language Model for Meeting Summarization.
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