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.

提出了一个将会议记录按照方面进行总结的双阶段方法，它首先通过模拟标注的方式在 AMI 语料库上训练了一个句子分类器来选择特定方面的相关内容，然后将选定的句子合并作为输入，使用总结器生成基于方面的总结，实验结果表明该方法在 AMI 语料库上优于多个强基线模型，从而证明了我们提出方法的有效性。