In this paper, we propose a controllable neural generation framework that can flexibly guide dialogue summarization with personal named entity planning. The conditional sequences are modulated to decide what types of information or what perspective to focus on when forming summaries to tackle the under-constrained problem in summarization tasks. This framework supports two types of use cases: (1) Comprehensive Perspective, which is a general-purpose case with no user-preference specified, considering summary points from all conversational interlocutors and all mentioned persons; (2) Focus Perspective, positioning the summary based on a user-specified personal named entity, which could be one of the interlocutors or one of the persons mentioned in the conversation. During training, we exploit occurrence planning of personal named entities and coreference information to improve temporal coherence and to minimize hallucination in neural generation. Experimental results show that our proposed framework generates fluent and factually consistent summaries under various planning controls using both objective metrics and human evaluations.

翻译：在本文中,我们提议一个可控制的神经生成框架,可灵活地指导对话与个人名称实体规划的总结; 有条件的顺序经过调整,以决定在制定摘要以解决总体任务中受限制不足的问题时,应侧重于何种类型的信息或观点; 这一框架支持两类使用案例:(1) 全面视角,这是一个没有用户首选的通用案例,考虑到所有对话者及所有被提及人员的简要观点;(2) 焦点视角,将摘要定位于用户指定的个人名称实体,该实体可以是对话者或对话中提及的人之一;在培训期间,我们利用个人名称实体的发生规划,并参考信息,以提高时间的一致性,尽量减少神经生成过程中的幻觉;实验结果显示,我们提议的框架在各种规划控制下,利用客观指标和人力评价,生成流畅和事实一致的摘要。