Private Meeting Summarization Without Performance Loss

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

Private Meeting Summarization Without Performance Loss. / Lee, Seolhwa; Søgaard, Anders.

SIGIR 2023 - Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, Inc., 2023. p. 2282-2286.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Lee, S & Søgaard, A 2023, Private Meeting Summarization Without Performance Loss. in SIGIR 2023 - Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, Inc., pp. 2282-2286, 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023, Taipei, Taiwan, Province of China, 23/07/2023. https://doi.org/10.1145/3539618.3592042

APA

Lee, S., & Søgaard, A. (2023). Private Meeting Summarization Without Performance Loss. In SIGIR 2023 - Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 2282-2286). Association for Computing Machinery, Inc.. https://doi.org/10.1145/3539618.3592042

Vancouver

Lee S, Søgaard A. Private Meeting Summarization Without Performance Loss. In SIGIR 2023 - Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, Inc. 2023. p. 2282-2286 https://doi.org/10.1145/3539618.3592042

Author

Lee, Seolhwa ; Søgaard, Anders. / Private Meeting Summarization Without Performance Loss. SIGIR 2023 - Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, Inc., 2023. pp. 2282-2286

Bibtex

@inproceedings{652747874b284e20b220e4bb2a6c9a80,

title = "Private Meeting Summarization Without Performance Loss",

abstract = "Meeting summarization has an enormous business potential, but in addition to being a hard problem, roll-out is challenged by privacy concerns. We explore the problem of meeting summarization under differential privacy constraints and find, to our surprise, that while differential privacy leads to slightly lower performance on in-sample data, differential privacy improves performance when evaluated on unseen meeting types. Since meeting summarization systems will encounter a great variety of meeting types in practical employment scenarios, this observation makes safe meeting summarization seem much more feasible. We perform extensive error analysis and identify potential risks in meeting summarization under differential privacy, including a faithfulness analysis.",

keywords = "Differential Privacy, Meeting Summarization, Text Summarization",

author = "Seolhwa Lee and Anders S{\o}gaard",

note = "Publisher Copyright: {\textcopyright} 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.; 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023 ; Conference date: 23-07-2023 Through 27-07-2023",

year = "2023",

doi = "10.1145/3539618.3592042",

language = "English",

pages = "2282--2286",

booktitle = "SIGIR 2023 - Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval",

publisher = "Association for Computing Machinery, Inc.",

}

RIS

TY - GEN

T1 - Private Meeting Summarization Without Performance Loss

AU - Lee, Seolhwa

AU - Søgaard, Anders

PY - 2023

Y1 - 2023

N2 - Meeting summarization has an enormous business potential, but in addition to being a hard problem, roll-out is challenged by privacy concerns. We explore the problem of meeting summarization under differential privacy constraints and find, to our surprise, that while differential privacy leads to slightly lower performance on in-sample data, differential privacy improves performance when evaluated on unseen meeting types. Since meeting summarization systems will encounter a great variety of meeting types in practical employment scenarios, this observation makes safe meeting summarization seem much more feasible. We perform extensive error analysis and identify potential risks in meeting summarization under differential privacy, including a faithfulness analysis.

AB - Meeting summarization has an enormous business potential, but in addition to being a hard problem, roll-out is challenged by privacy concerns. We explore the problem of meeting summarization under differential privacy constraints and find, to our surprise, that while differential privacy leads to slightly lower performance on in-sample data, differential privacy improves performance when evaluated on unseen meeting types. Since meeting summarization systems will encounter a great variety of meeting types in practical employment scenarios, this observation makes safe meeting summarization seem much more feasible. We perform extensive error analysis and identify potential risks in meeting summarization under differential privacy, including a faithfulness analysis.

KW - Differential Privacy

KW - Meeting Summarization

KW - Text Summarization

U2 - 10.1145/3539618.3592042

DO - 10.1145/3539618.3592042

M3 - Article in proceedings

AN - SCOPUS:85168667313

SP - 2282

EP - 2286

BT - SIGIR 2023 - Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

PB - Association for Computing Machinery, Inc.

T2 - 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023

Y2 - 23 July 2023 through 27 July 2023

ER -

ID: 366985696

Department of Computer Science