Segment-level Tree Search for Long Meeting Document Summarization
📰 ArXiv cs.AI
Learn to summarize long meeting documents using segment-level tree search, improving upon traditional multi-stage pipelines
Action Steps
- Apply segment-level tree search to meeting documents to reduce error propagation
- Use Monte Carlo methods to validate intermediate results
- Configure a multi-stage pipeline with intermediate validation to improve summarization accuracy
- Test the proposed approach on a dataset of long meeting documents
- Compare the results with traditional summarization methods to evaluate the improvement
Who Needs to Know This
NLP researchers and engineers working on document summarization tasks can benefit from this approach to improve the accuracy of their models
Key Insight
💡 Segment-level tree search can reduce cumulative error propagation in document summarization
Share This
📄 Summarize long meeting docs more accurately with segment-level tree search! 🚀
Key Takeaways
Learn to summarize long meeting documents using segment-level tree search, improving upon traditional multi-stage pipelines
Full Article
Title: Segment-level Tree Search for Long Meeting Document Summarization
Abstract:
arXiv:2606.08445v1 Announce Type: cross 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
Abstract:
arXiv:2606.08445v1 Announce Type: cross 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
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