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