Private Meeting Summarization Without Performance Loss
Seolhwa Lee Anders Søgaard
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.