  Current disfluency detection models focus on individual utterances each from
a single speaker. However, numerous discontinuity phenomena in spoken
conversational transcripts occur across multiple turns, hampering human
readability and the performance of downstream NLP tasks. This study addresses
these phenomena by proposing an innovative Multi-Turn Cleanup task for spoken
conversational transcripts and collecting a new dataset, MultiTurnCleanup1. We
design a data labeling schema to collect the high-quality dataset and provide
extensive data analysis. Furthermore, we leverage two modeling approaches for
experimental evaluation as benchmarks for future research.
