  Meeting minutes record any subject matters discussed, decisions reached and
actions taken at meetings. The importance of minuting cannot be overemphasized
in a time when a significant number of meetings take place in the virtual
space. In this paper, we present a sliding window approach to automatic
generation of meeting minutes. It aims to tackle issues associated with the
nature of spoken text, including lengthy transcripts and lack of document
structure, which make it difficult to identify salient content to be included
in the meeting minutes. Our approach combines a sliding window and a neural
abstractive summarizer to navigate through the transcripts to find salient
content. The approach is evaluated on transcripts of natural meeting
conversations, where we compare results obtained for human transcripts and two
versions of automatic transcripts and discuss how and to what extent the
summarizer succeeds at capturing salient content.
