  Conversational question generation (CQG) serves as a vital task for machines
to assist humans, such as interactive reading comprehension, through
conversations. Compared to traditional single-turn question generation (SQG),
CQG is more challenging in the sense that the generated question is required
not only to be meaningful, but also to align with the occurred conversation
history. While previous studies mainly focus on how to model the flow and
alignment of the conversation, there has been no thorough study to date on
which parts of the context and history are necessary for the model. We argue
that shortening the context and history is crucial as it can help the model to
optimise more on the conversational alignment property. To this end, we propose
CoHS-CQG, a two-stage CQG framework, which adopts a CoHS module to shorten the
context and history of the input. In particular, CoHS selects contiguous
sentences and history turns according to their relevance scores by a top-p
strategy. Our model achieves state-of-the-art performances on CoQA in both the
answer-aware and answer-unaware settings.
