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.

基于 GNN 的节点分类模型，利用话语结构从复杂的多方讨论中筛选出最重要的话语，并生成提取式摘要，实验证明该方法超过现有的基于文本和图的提取式摘要系统。

利用话语结构创建会议摘要

Leveraging discourse structure for the creation of meeting extracts

Previous stance detection studies typically concentrate on evaluating stances
within individual instances, thereby exhibiting limitations in effectively
modeling multi-party discussions concerning the same specific topic, as
naturally transpire in authentic social media interactions. This constraint
arises primarily due to the scarcity of datasets that authentically replicate
real social media contexts, hindering the research progress of conversational
stance detection. In this paper, we introduce a new multi-turn conversation
stance detection dataset (called \textbf{MT-CSD}), which encompasses multiple
targets for conversational stance detection. To derive stances from this
challenging dataset, we propose a global-local attention network
(\textbf{GLAN}) to address both long and short-range dependencies inherent in
conversational data. Notably, even state-of-the-art stance detection methods,
exemplified by GLAN, exhibit an accuracy of only 50.47\%, highlighting the
persistent challenges in conversational stance detection. Furthermore, our
MT-CSD dataset serves as a valuable resource to catalyze advancements in
cross-domain stance detection, where a classifier is adapted from a different
yet related target. We believe that MT-CSD will contribute to advancing
real-world applications of stance detection research. Our source code, data,
and models are available at https://github.com/nfq729/MT-CSD.

本文介绍了一个新的多轮对话立场检测数据集（称为 MT-CSD），提出了一种全局 - 局部注意网络（GLAN）来解决对话数据中存在的长距离和短距离依赖性，还讨论了跨领域立场检测的挑战和对进展的贡献。