[2509.26435] Adaptive Planning for Multi-Attribute Controllable Summarization with Monte Carlo Tree Search

Computer Science > Computation and Language arXiv:2509.26435 (cs) [Submitted on 30 Sep 2025 (v1), last revised 10 Apr 2026 (this version, v2)] Title:Adaptive Planning for Multi-Attribute Controllable Summarization with Monte Carlo Tree Search Authors:Sangwon Ryu, Heejin Do, Yunsu Kim, Gary Geunbae Lee, Jungseul Ok View a PDF of the paper titled Adaptive Planning for Multi-Attribute Controllable Summarization with Monte Carlo Tree Search, by Sangwon Ryu and 4 other authors View PDF HTML (experimental) Abstract: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 training-free framework that reframes the task as planning the order of sequential attribute control with a customized Monte Carlo Tree Search (MCTS). In PACO, nodes represent summaries, and actions correspond to single-attribute adjustments, enabling progressive refinement of only the attributes requiring further control. This strategy adaptively discovers optimal control orders, ultimately producing summaries that effectively meet all constraints. Extensive experiments across diverse domai...