  Text segmentation is important for signaling a document's structure. Without
segmenting a long document into topically coherent sections, it is difficult
for readers to comprehend the text, let alone find important information. The
problem is only exacerbated by a lack of segmentation in transcripts of
audio/video recordings. In this paper, we explore the role that section
segmentation plays in extractive summarization of written and spoken documents.
Our approach learns robust sentence representations by performing summarization
and segmentation simultaneously, which is further enhanced by an
optimization-based regularizer to promote selection of diverse summary
sentences. We conduct experiments on multiple datasets ranging from scientific
articles to spoken transcripts to evaluate the model's performance. Our
findings suggest that the model can not only achieve state-of-the-art
performance on publicly available benchmarks, but demonstrate better
cross-genre transferability when equipped with text segmentation. We perform a
series of analyses to quantify the impact of section segmentation on
summarizing written and spoken documents of substantial length and complexity.
