Segment-level Tree Search for Long Meeting Document Summarization

2026-06-07

AI Summary

This work addresses the challenges of summarizing lengthy and structurally complex meeting documents, where existing approaches suffer from error propagation in multi-stage pipelines and are constrained by short, low-quality reference summaries. To overcome these limitations, the authors propose the S3 framework, which introduces Monte Carlo Tree Search (MCTS) to the meeting summarization task for the first time. S3 generates multiple candidate segment-level summaries as tree nodes and employs a self-reward mechanism to guide the search process toward optimal combinations, without requiring additional model training. This approach effectively mitigates error accumulation and, despite relying solely on a 7B-parameter large language model, produces summaries of moderate length and quality comparable to those generated by a 72B-parameter model.

Abstract

Meeting documents are challenging to summarize due to their length and complex conversational structure. Existing approaches typically adopt multi-stage pipelines that extract information prior to summarization; however, these approaches often suffer from cumulative error propagation without intermediate validation, a limitation further amplified by short and low-quality reference summaries. We propose segment-level summarization via Monte Carlo Tree Search (S3), a training-free framework that constructs a final summary by composing segment-level summary candidates. S3 partitions a long document into segments and generates multiple summary candidates per segment, forming nodes of a search tree. The best-scoring combination is selected via self-reward-guided tree search and refined into the final output. Despite using a 7B model, S3 achieves performance comparable to larger 72B models while producing length-appropriate summaries.

Problem

• meeting document summarization
• long document
• conversational structure
• error propagation
• reference summary quality

Innovation

• segment-level summarization
• Monte Carlo Tree Search
• self-reward
• training-free framework
• long meeting summarization