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

• URL: http://arxiv.org/abs/2509.15901v1
• Date: Fri, 19 Sep 2025 11:58:17 GMT
• Title: Re-FRAME the Meeting Summarization SCOPE: Fact-Based Summarization and Personalization via Questions
• Authors: Frederic Kirstein, Sonu Kumar, Terry Ruas, Bela Gipp,
• Abstract summary: We present FRAME, a modular pipeline that reframes summarization as a semantic enrichment task.<n> FRAME extracts and scores salient facts, organizes them thematically, and uses these to enrich an outline into an abstractive summary.<n>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.
• Score: 9.381476258394175
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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