Aspect-based Meeting Transcript Summarization: A Two-Stage Approach with
  Weak Supervision on Sentence Classification
Zhongfen Deng Seunghyun Yoon Trung Bui Franck Dernoncourt Quan Hung Tran Shuaiqi Liu Wenting Zhao Tao Zhang Yibo Wang Philip S. Yu
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.