Emergency and non-emergency response systems are essential services provided
by local governments and critical to protecting lives, the environment, and
property. The effective handling of (non-)emergency calls is critical for
public safety and well-being. By reducing the burden through non-emergency
callers, residents in critical need of assistance through 911 will receive a
fast and effective response. Collaborating with the Department of Emergency
Communications (DEC) in Nashville, we analyzed 11,796 non-emergency call
recordings and developed Auto311, the first automated system to handle 311
non-emergency calls, which (1) effectively and dynamically predicts ongoing
non-emergency incident types to generate tailored case reports during the call;
(2) itemizes essential information from dialogue contexts to complete the
generated reports; and (3) strategically structures system-caller dialogues
with optimized confidence. We used real-world data to evaluate the system's
effectiveness and deployability. The experimental results indicate that the
system effectively predicts incident type with an average F-1 score of 92.54%.
Moreover, the system successfully itemizes critical information from relevant
contexts to complete reports, evincing a 0.93 average consistency score
compared to the ground truth. Additionally, emulations demonstrate that the
system effectively decreases conversation turns as the utterance size gets more
extensive and categorizes the ongoing call with 94.49% mean accuracy.

利用实际数据评估了 Auto311 系统的有效性和可操作性，实验结果表明该系统可有效预测事件类型、从会话背景中提取关键信息，并能够减少对话轮数，分类准确率为 94.49%。