  Abstractive dialogue summarization is to generate a concise and fluent
summary covering the salient information in a dialogue among two or more
interlocutors. It has attracted great attention in recent years based on the
massive emergence of social communication platforms and an urgent requirement
for efficient dialogue information understanding and digestion. Different from
news or articles in traditional document summarization, dialogues bring unique
characteristics and additional challenges, including different language styles
and formats, scattered information, flexible discourse structures and unclear
topic boundaries. This survey provides a comprehensive investigation on
existing work for abstractive dialogue summarization from scenarios, approaches
to evaluations. It categorizes the task into two broad categories according to
the type of input dialogues, i.e., open-domain and task-oriented, and presents
a taxonomy of existing techniques in three directions, namely, injecting
dialogue features, designing auxiliary training tasks and using additional
data.A list of datasets under different scenarios and widely-accepted
evaluation metrics are summarized for completeness. After that, the trends of
scenarios and techniques are summarized, together with deep insights on
correlations between extensively exploited features and different scenarios.
Based on these analyses, we recommend future directions including more
controlled and complicated scenarios, technical innovations and comparisons,
publicly available datasets in special domains, etc.
