Abstract
We introduce an extractive summarization system for meetings that leverages discourse structure to better identify salient information from complex multi-party discussions. Using discourse graphs to represent semantic relations between the contents of utterances in a meeting, we train a GNN-based node classification model to select the most important utterances, which are then combined to create an extractive summary. Experimental results on AMI and ICSI demonstrate that our approach surpasses existing text-based and graph-based extractive summarization systems, as measured by both classification and summarization metrics. Additionally, we conduct ablation studies on discourse structure and relation type to provide insights for future NLP applications leveraging discourse analysis theory.
Abstract (translated)
我们提出了一个会议提取总结系统，该系统利用会话结构更好地识别复杂多方讨论中的显眼信息。通过使用语义图表示会话内容之间的语义关系，我们训练了一个基于图神经网络的节点分类模型来选择最重要的会话内容，然后将这些内容进行结合以创建提取式摘要。在AMI和ICSI实验中，我们的方法证明了我们的方法超越了现有的基于文本和图的提取式总结系统，这是通过分类和总结指标来衡量的。此外，我们对会话结构和关系类型进行了消融研究，为未来的NLP应用利用会话分析理论提供了洞察。
URL
https://arxiv.org/abs/2405.11055
