Private Meeting Summarization Without Performance Loss
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Documents
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Submitted manuscript, 1.24 MB, PDF document
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.
Original language | English |
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Title of host publication | SIGIR 2023 - Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval |
Number of pages | 5 |
Publisher | Association for Computing Machinery, Inc. |
Publication date | 2023 |
Pages | 2282-2286 |
ISBN (Electronic) | 9781450394086 |
DOIs | |
Publication status | Published - 2023 |
Event | 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023 - Taipei, Taiwan, Province of China Duration: 23 Jul 2023 → 27 Jul 2023 |
Conference
Conference | 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023 |
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Land | Taiwan, Province of China |
By | Taipei |
Periode | 23/07/2023 → 27/07/2023 |
Sponsor | ACM SIGIR |
Bibliographical note
Publisher Copyright:
© 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.
- Differential Privacy, Meeting Summarization, Text Summarization
Research areas
ID: 366985696