Controllable Summarization with Constrained M arkov Decision Process (2021)

Abstract: 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.1

## human evaluation we conduct human evaluation to verify the quality of the generated summaries. we hire postgraduate ## conclusion we propose a novel cmdp training framework for controllable text summarization. our framework imposes constraints on the training objective to explicitly disallow the output summaries from violating the requirement specified by users. moreover, we apply our framework to control key summarization attributes such as length, covered entities, and abstractiveness of the summaries. we then devise specific constraints to restrict each of these attributes respectively. empirical studies on popular benchmarks demonstrate that our framework significantly improves the capability of controllable summarization models to conform to the desired attribute requirement. in our framework, we can set hard constraints without tuning threshold values. for instance, we set the threshold of our length bin constraint to 0 to disallow the violation of length bin requirement. compared to the weights of penalty in the mdp framework, the threshold value in a soft constraint is also easier to set. for example, the goal of entity control is to generate a summary that presents the key information of the requested entities, which implies that the generated summaries should obtain a high qa-f1 score. the range of qa-f1 score is [0, 1]. in order to encourage the generated summaries to obtain a high qa-f1 score, the threshold for qa-f1 score should be close to 1, this gives us a clue about how to set the value of threshold. on the other hand, the mdp framework does not give us any clues to set the values of penalty weights. in summary, our cmdp framework needs to tune one threshold value for entity control and it does not need to tune any threshold for other control settings. whereas the numbers of penalty weights to be tuned in the mdp framework are 2, 3, and 3 for length, entity, and abstractiveness control respectively.