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

arXiv · 2311.04292

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

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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.

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BibTeX RIS

Zhongfen Deng, Seunghyun Yoon, Trung Bui, Franck Dernoncourt, Quan Hung Tran, Shuaiqi Liu, Wenting Zhao, Tao Zhang, Yibo Wang, Philip S. Yu. 2023-11-07. Aspect-based Meeting Transcript Summarization: A Two-Stage Approach with Weak Supervision on Sentence Classification. https://arxiv.org/abs/2311.04292

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