Adaptive Planning for Multi-Attribute Controllable Summarization with Monte Carlo Tree Search
Year: 2026
Type: conference-paper
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 perattribute fine-tuning, limiting flexibility across diverse summary attributes.In this paper, we propose adaptive planning for multi-attribute controllable summarization (PACO), a trainingfree 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 domains and models demonstrate that PACO achieves robust multi-attribute controllability, surpassing both LLM-based self-planning models and finetuned baselines.Remarkably, PACO with Llama-3.2-1Brivals the controllability of the much larger Llama-3.3-70Bbaselines.With larger models, PACO achieves superior control performance, outperforming all competitors.
Authors: Sangwon Ryu, Heejin Do, Yunsu Kim, Gary Lee, Jungseul Ok
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166835640