  Aspect-based meeting transcript summarization aims to produce multiple
summaries, each focusing on one aspect of content in a meeting transcript. It
is challenging as sentences related to different aspects can mingle together,
and those relevant to a specific aspect can be scattered throughout the long
transcript of a meeting. The traditional summarization methods produce one
summary mixing information of all aspects, which cannot deal with the above
challenges of aspect-based meeting transcript summarization. In this paper, we
propose a two-stage method for aspect-based meeting transcript summarization.
To select the input content related to specific aspects, we train a sentence
classifier on a dataset constructed from the AMI corpus with pseudo-labeling.
Then we merge the sentences selected for a specific aspect as the input for the
summarizer to produce the aspect-based summary. Experimental results on the AMI
corpus outperform many strong baselines, which verifies the effectiveness of
our proposed method.
