  Document summarization, as a fundamental task in natural language generation,
aims to generate a short and coherent summary for a given document.
Controllable summarization, especially of the length, is an important issue for
some practical applications, especially how to trade-off the length constraint
and information integrity. In this paper, we propose an \textbf{A}daptive
\textbf{L}ength \textbf{C}ontrolling \textbf{O}ptimization (\textbf{ALCO})
method to leverage two-stage abstractive summarization model via reinforcement
learning. ALCO incorporates length constraint into the stage of sentence
extraction to penalize the overlength extracted sentences. Meanwhile, a
saliency estimation mechanism is designed to preserve the salient information
in the generated sentences. A series of experiments have been conducted on a
wildly-used benchmark dataset \textit{CNN/Daily Mail}. The results have shown
that ALCO performs better than the popular baselines in terms of length
controllability and content preservation.
