Adaptive Planning for Multi-Attribute Controllable Summarization with Monte Carlo Tree Search

ArXi:2509.26435v2 Announce Type: replace-cross Controllable summarization moves beyond generic outputs toward human-aligned summaries guided by specified attributes. In practice, the interdependence among attributes makes it challenging for language models to satisfy correlated constraints consistently. Moreover, previous approaches often require per-attribute fine-tuning, limiting flexibility across diverse summary attributes. In this paper, we propose adaptive planning for multi-attribute controllable summarization (PACO), a