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Dialogue summarization has been extensively studied and applied, where theprior works mainly focused on exploring superior model structures to align theinput dialogue and the output summary. However, for professional dialogues(e.g., legal debate and medical diagnosis), semantic/statistical alignment canhardly fill the logical/factual gap between input dialogue discourse andsummary output with external knowledge. In this paper, we mainly investigatethe factual inconsistency problem for Dialogue Inspectional Summarization (DIS)under non-pretraining and pretraining settings. An innovative end-to-enddialogue summary generation framework is proposed with two auxiliary tasks:Expectant Factual Aspect Regularization (EFAR) and Missing Factual EntityDiscrimination (MFED). Comprehensive experiments demonstrate that the proposedmodel can generate a more readable summary with accurate coverage of factualaspects as well as informing the user with potential missing facts detectedfrom the input dialogue for further human intervention.

Dialogue Inspectional Summarization with Factual Inconsistency Awareness