Reinforced Abstractive Summarization with Adaptive Length Controlling. (arXiv:2112.07534v5 [cs.CL] UPDATED)

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 …

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