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Current disfluency detection models focus on individual utterances each froma single speaker. However, numerous discontinuity phenomena in spokenconversational transcripts occur across multiple turns, hampering humanreadability and the performance of downstream NLP tasks. This study addressesthese phenomena by proposing an innovative Multi-Turn Cleanup task for spokenconversational transcripts and collecting a new dataset, MultiTurnCleanup1. Wedesign a data labeling schema to collect the high-quality dataset and provideextensive data analysis. Furthermore, we leverage two modeling approaches forexperimental evaluation as benchmarks for future research.

MultiTurnCleanup: A Benchmark for Multi-Turn Spoken Conversational Transcript Cleanup