Segment-level Tree Search for Long Meeting Document Summarization

• URL: http://arxiv.org/abs/2606.08445v1
• Date: Sun, 07 Jun 2026 04:21:23 GMT
• Title: Segment-level Tree Search for Long Meeting Document Summarization
• Abstract summary: We propose segment-level summarization via Monte Carlo Tree Search (S3), a training-free framework.<n>S3 partitions a long document into segments and generates multiple summary candidates per segment, forming nodes of a search tree.<n>Despite using a 7B model, S3 achieves performance comparable to larger 72B models while producing length-appropriate summaries.
• Score: 40.841609062628216
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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