Segment-level Tree Search for Long Meeting Document Summarization
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...
| Authors | Sangwon Ryu, Heejin Do, Jun Seo et al. |
|---|---|
| Primary category | cs.CL |
| Published | 7 June 2026 |
| Last updated at source | 7 June 2026 |
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