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In this paper, we propose a controllable neural generation framework that canflexibly guide dialogue summarization with personal named entity planning. Theconditional sequences are modulated to decide what types of information or whatperspective to focus on when forming summaries to tackle the under-constrainedproblem in summarization tasks. This framework supports two types of use cases:(1) Comprehensive Perspective, which is a general-purpose case with nouser-preference specified, considering summary points from all conversationalinterlocutors and all mentioned persons; (2) Focus Perspective, positioning thesummary based on a user-specified personal named entity, which could be one ofthe interlocutors or one of the persons mentioned in the conversation. Duringtraining, we exploit occurrence planning of personal named entities andcoreference information to improve temporal coherence and to minimizehallucination in neural generation. Experimental results show that our proposedframework generates fluent and factually consistent summaries under variousplanning controls using both objective metrics and human evaluations.

Controllable Neural Dialogue Summarization with Personal Named Entity Planning