Throughout a conversation, the way participants interact with each other is
in constant flux: their tones may change, they may resort to different
strategies to convey their points, or they might alter their interaction
patterns. An understanding of these dynamics can complement that of the actual
facts and opinions discussed, offering a more holistic view of the trajectory
of the conversation: how it arrived at its current state and where it is likely
heading.
In this work, we introduce the task of summarizing the dynamics of
conversations, by constructing a dataset of human-written summaries, and
exploring several automated baselines. We evaluate whether such summaries can
capture the trajectory of conversations via an established downstream task:
forecasting whether an ongoing conversation will eventually derail into toxic
behavior. We show that they help both humans and automated systems with this
forecasting task. Humans make predictions three times faster, and with greater
confidence, when reading the summaries than when reading the transcripts.
Furthermore, automated forecasting systems are more accurate when constructing,
and then predicting based on, summaries of conversation dynamics, compared to
directly predicting on the transcripts.

我们引入了动态对话总结的任务，通过构建一个人为编写的总结数据集并探索几种自动化基准来总结对话的动态。我们评估了这些总结是否能够通过一个已建立的下游任务 —— 预测正在进行的对话是否最终会发展成有害行为 —— 来捕捉对话的轨迹。我们证明这些总结对人类和自动化系统在预测任务上都有帮助。人们在阅读总结时，做出预测的速度是在阅读完整对话的三倍，并且更有信心。此外，与直接对对话记录进行预测相比，自动化预测系统在构建并基于对话动态总结进行预测时更加准确。