Speech is arguably the most basic, most natural form of communication that we engage in, so it should come as no surprise that there has been a consistent pressure to deliver spoken audio content on web pages that, in principle, can be searched through. Even once the search problem is solved, however, the low-bandwidth, non-visual, traditional delivery of spoken audio makes it much more difficult to _browse_ through. This makes the automated summarization of speech particularly attractive: given a number N, prepare a summary of a spoken "document" that contains the most important or salient content that is N seconds long, or N utterances long, or N percent of the original document's length. This talk will present a (human-prepared) summary of recent research on summarizing speech. We'll talk about how speech summarization is usually evaluated, including some of the appropriate baselines in this area, the dependence of genre on the performance and tuning of summarizers, the role of automated speech transcription in summarization, and the usefulness of some of the acoustic, untranscribed features of the speech signal.