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.

本文提出了一种可控的神经生成框架，它可以利用个人命名实体规划灵活引导对话摘要生成。条件序列被调节以决定何种类型的信息或者何种角度着重于形成摘要，以解决摘要任务中的低限制问题。实验结果表明，我们提出的框架使用各种规划控制生成流畅和事实正确的摘要。

可控的神经对话摘要生成中的个性化命名实体规划