Reader Comes First: A Demand-Oriented Readability Controllable Summarization

Readability controllable summarization aims to identify different readers' readability demands and generate summaries that meet their needs better. Existing methods typically rely on fixed prefix control tags to generate summaries at specific readability levels, which confines them to controlling readability at a coarse level. In this work, we argue that they overlook a key aspect of this task, i.e., recognizing readers' demands regarding the summary content behind readability preferences. These demands vary across instances and readability levels, making them a natural source of fine-grained control signals. Therefore, in this paper, instead of using pre-defined control tags to manage the readability, we first identify readers' varying demands in the form of QA pairs, and then explore demand-oriented controllable summarization for different readability levels, by proposing three methods: (1) demand-enhanced instruction tuning, (2) least-to-most prompting with the concentration of reader demands as a sub-problems chain, and (3) a decoding approach that promotes the model's attention to demand-related information in context for better demand modeling. Compared with previous methods, ours can better distinguish between different readability levels, enabling fine-grained readability control. Experimental results on two public datasets demonstrate the effectiveness and robustness of our methods.1