# Re-FRAME the Meeting Summarization SCOPE: Fact-Based Summarization and Personalization via Questions

arXiv: https://arxiv.org/abs/2509.15901 · HTML: https://mlsanity.com/paper/2509.15901/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2025.

## Built on by (most cited first, 3 of 3)

- [NLP for Local Governance Meeting Records: A Focus Article on Tasks, Datasets, Metrics and Benchmark](https://mlsanity.com/paper/2602.08162/) (2026)
- [LOOMSUM: Weaving Quantitative and Narrative Evidence for Faithful Long Text-Table Summarization](https://mlsanity.com/paper/2609.00241/) (2026)
- [One Prompt Does Not Fit All: Self-Meta-Evolve for Personalized Information Extraction](https://mlsanity.com/paper/2609.21626/) (2026)

## Abstract (arXiv)

Meeting summarization with large language models (LLMs) remains error-prone, often producing outputs with hallucinations, omissions, and irrelevancies. We present FRAME, a modular pipeline that reframes summarization as a semantic enrichment task. FRAME extracts and scores salient facts, organizes them thematically, and uses these to enrich an outline into an abstractive summary. To personalize summaries, we introduce SCOPE, a reason-out-loud protocol that has the model build a reasoning trace by answering nine questions before content selection. For evaluation, we propose P-MESA, a multi-dimensional, reference-free evaluation framework to assess if a summary fits a target reader. P-MESA reliably identifies error instances, achieving >= 89% balanced accuracy against human annotations and strongly aligns with human severity ratings (r >= 0.70). On QMSum and FAME, FRAME reduces hallucination and omission by 2 out of 5 points (measured with MESA), while SCOPE improves knowledge fit and goal alignment over prompt-only baselines. Our findings advocate for rethinking summarization to improve control, faithfulness, and personalization.