Meeting minutes record any subject matter discussed, decisions reached and actions taken at the meeting. The importance of automatic minuting cannot be overstated. In this paper, we present a sliding window approach to automatic generation of meeting
minutes. It aims at addressing issues pertaining to the nature of spoken text, including the lengthy transcript and lack of document structure, which make it difficult to identify salient content to be included in meeting minutes. Our approach combines a sliding-window approach and a neural abstractive summarizer to navigate through the raw transcript 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.
We propose semantic visualization as a linguistic visual analytic method. It can enable exploration and discovery over large datasets of complex networks by exploiting the semantics of the relations in them. This involves extracting information, appl
ying parameter reduction operations, building hierarchical data representation and designing visualization. We also present the accompanying COVID-SemViz a searchable and interactive visualization system for knowledge exploration of COVID-19 data to demonstrate the application of our proposed method. In the user studies, users found that semantic visualization-powered COVID-SemViz is helpful in terms of finding relevant information and discovering unknown associations.
