Aspect-based Meeting Transcript Summarization: A Two-Stage Approach with Weak Supervision on Sentence Classification

TL;DR

This paper introduces a two-stage approach for aspect-based meeting transcript summarization, utilizing weak supervision for sentence classification to generate focused summaries for different aspects of a meeting.

Contribution

It presents a novel two-stage method combining weakly supervised sentence classification with summarization, improving aspect-specific summaries over traditional methods.

Findings

Outperforms several strong baselines on the AMI corpus

Effective sentence classification using pseudo-labeling

Improved aspect-based summarization quality

Abstract

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.