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

2025-09-30

AI Summary

This paper addresses the challenge of inconsistent attribute control in multi-attribute controllable summarization, arising from inter-attribute dependencies. We propose PACO, a training-free adaptive planning framework. Its core innovation is the first application of Monte Carlo Tree Search (MCTS) to controllable summarization, formulating multi-attribute optimization as a sequential planning problem: nodes represent summary states, actions correspond to incremental adjustments of individual attributes, and language-model-guided dynamic search automatically discovers the optimal control sequence. PACO requires no fine-tuning and flexibly adapts to arbitrary attribute combinations. Empirically, it significantly improves control accuracy and cross-domain generalization across diverse models and domains: using only Llama-3.2-1B, it matches the performance of 70B-scale baselines; with larger models, it consistently outperforms state-of-the-art methods.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: Summarization
• Multiagent Systems: Multiagent Planning

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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 domains and models demonstrate that PACO achieves robust multi-attribute controllability, surpassing both LLM-based self-planning models and fine-tuned baselines. Remarkably, PACO with Llama-3.2-1B rivals the controllability of the much larger Llama-3.3-70B baselines. With larger models, PACO achieves superior control performance, outperforming all competitors.

Problem

• Addresses interdependent attribute constraints in controllable summarization
• Eliminates need for per-attribute fine-tuning in summary generation
• Optimizes sequential attribute control order using adaptive planning

Innovation

• Training-free framework using Monte Carlo Tree Search
• Adaptively plans optimal order of attribute control
• Progressively refines summaries through single-attribute adjustments