We study controllable text summarization which allows users to gain control
on a particular attribute (e.g., length limit) of the generated summaries. In
this work, we propose a novel training framework based on Constrained Markov
Decision Process (CMDP), which conveniently includes a reward function along
with a set of constraints, to facilitate better summarization control. The
reward function encourages the generation to resemble the human-written
reference, while the constraints are used to explicitly prevent the generated
summaries from violating user-imposed requirements. Our framework can be
applied to control important attributes of summarization, including length,
covered entities, and abstractiveness, as we devise specific constraints for
each of these aspects. Extensive experiments on popular benchmarks show that
our CMDP framework helps generate informative summaries while complying with a
given attribute's requirement.

本研究通过引入基于约束马尔可夫决策过程（CMDP）的训练框架，允许用户对生成的摘要进行特定属性（例如长度限制）的控制，该框架旨在通过奖励函数和一组约束来提高摘要生成的可控性。实验结果表明，我们的 CMDP 框架有助于生成具有信息量的摘要，并符合用户对属性的要求。