  Sentence Boundary Detection (SBD) has been a major research topic since
Automatic Speech Recognition transcripts have been used for further Natural
Language Processing tasks like Part of Speech Tagging, Question Answering or
Automatic Summarization. But what about evaluation? Do standard evaluation
metrics like precision, recall, F-score or classification error; and more
important, evaluating an automatic system against a unique reference is enough
to conclude how well a SBD system is performing given the final application of
the transcript? In this paper we propose Window-based Sentence Boundary
Evaluation (WiSeBE), a semi-supervised metric for evaluating Sentence Boundary
Detection systems based on multi-reference (dis)agreement. We evaluate and
compare the performance of different SBD systems over a set of Youtube
transcripts using WiSeBE and standard metrics. This double evaluation gives an
understanding of how WiSeBE is a more reliable metric for the SBD task.
