@inproceedings{kirstein-etal-2025-frame,
title = "Re-{FRAME} the Meeting Summarization {SCOPE}: Fact-Based Summarization and Personalization via Questions",
author = "Kirstein, Frederic and
Kumar, Sonu and
Ruas, Terry and
Gipp, Bela",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.1094/",
doi = "10.18653/v1/2025.findings-emnlp.1094",
pages = "20087--20137",
ISBN = "979-8-89176-335-7",
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 $\geq 89%$ balanced accuracy against human annotations and strongly aligned with human severity ratings ($\rho \geq 0.70$). 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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<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 \geq 89% balanced accuracy against human annotations and strongly aligned with human severity ratings (ρ \geq 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.</abstract>
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%0 Conference Proceedings
%T Re-FRAME the Meeting Summarization SCOPE: Fact-Based Summarization and Personalization via Questions
%A Kirstein, Frederic
%A Kumar, Sonu
%A Ruas, Terry
%A Gipp, Bela
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F kirstein-etal-2025-frame
%X 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 \geq 89% balanced accuracy against human annotations and strongly aligned with human severity ratings (ρ \geq 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.
%R 10.18653/v1/2025.findings-emnlp.1094
%U https://aclanthology.org/2025.findings-emnlp.1094/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.1094
%P 20087-20137
Markdown (Informal)
[Re-FRAME the Meeting Summarization SCOPE: Fact-Based Summarization and Personalization via Questions](https://aclanthology.org/2025.findings-emnlp.1094/) (Kirstein et al., Findings 2025)
ACL