Private Meeting Summarization Without Performance Loss
This addresses privacy concerns for businesses deploying meeting summarization systems, though it appears incremental in its approach.
The researchers tackled meeting summarization under differential privacy constraints and found that while it slightly reduces performance on in-sample data, it improves performance on unseen meeting types, making safe deployment more feasible.
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.