Re- FRAME the Meeting Summarization SCOPE : Fact-Based Summarization and Personalization via Questions (2025)

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.

## introduction meetings can be dense, chaotic, and high-stakes. summarizing them effectively is a natural language processing (nlp) challenge with value for corporate, academic, and governmental contexts @xcite @xcite . yet current large language model (llm)-based systems @xcite continue to produce summaries that omit key points, hallucinate content, and struggle with relevance @xcite . we argue that these weaknesses stem from a more profound structural mismatch that cur1 resources are available as per appendix a.1 on github. meetings hold three challenges compared to traditional structured texts @xcite : (1) salient content is scattered across speaker turns (information distribution), (2) utterances depend on long-range context (contextual dependencies), and (3) salience varies per reader (salience ambiguity). current summarization methods are not aligned with these properties. approaches relying on structural cues (e.g., sections, paragraphs) (liu and lapata, 2019) are unsuitable for meetings' boundary-free nature. chunking methods @xcite struggle with cross-chunk dependencies, while hybrid extractive-abstractive models @xcite ) may lose interpretability. each approach shares the same limitation: condensing information without reconstructing the underlying semantic structure. our frame (fact-based reconstruction and abstractive meeting summarization) framework is a fact-centric pipeline that reframes the established meeting summarization workflow as an enrichment task (shown in figure 1 ). drawing on research in fact extraction @xcite and summary planning @xcite , frame mimics how humans summarize texts in four stages @xcite : we extract self-contained verifiable facts to cut filler content (fact identification), filter them by salience (note-taking), organize them into an outline by thematic relationships (organization), and enrich these outlines to abstractive summaries strictly using facts (summary writing). with gpt-4o (ope-nai, 2024), frame improves output quality compared to recent approaches. on qmsum @xcite and @xcite , frame cuts hallucination by up to 3 out of 5 points (3→1, 4→1; lower is better), and irrelevance by 1 point (2→1, 3→1), beating gpt-4o and gemini-1.5 pro 2 (google et al., 2024) across six of eight mesa dimensions @xcite . salience ambiguity remains widely unaddressed by current systems, despite the growing interest in personalization. meetings involve diverse roles and objectives, yet most systems produce a onesize-fits-all output, ignoring role-specific goals and expertise @xcite . we introduce the scope protocol (summarizing content oriented to personal expectations) that guides an llm through an explicit reason-out-loud approach before scoring facts. drawing on cognitive science research @xcite , scope has the model answer a questionnaire about the reader's goals, expertise, and understanding. this creates an explicit reasoning trace to ground content selection, improving personalization. as evaluating personalized summaries is hard with existing metrics (e.g., @xcite , mesa), we propose p-mesa (personalized mesa), a reference-free llm-based personalization metric with seven dimensions: factuality, completeness, relevance, goal alignment, prioritization, knowledge-fit, and contextual framing. p-mesa correlates strongly with human judgment (avg. spearman ρ = 0.76) on 50 llm-generated summaries. on our new p-mesa metric (5-point likert score on quality impact), scope improves knowledge-level fit (2→1) and goal alignment (3→2), reducing oversimplification and reader hallucination. as such, scope outperforms roleplaying @xcite and reader-tailoring prompting (ghodratnama and zakershahrak, 2024) by modeling why information matters, not just to whom. 2 we will refer to them as gpt and gemini. this paper makes three key contributions: • frame: a modular summarization pipeline that treats summarization as enrichment, improving factuality, coherence, and salience handling. ## related work meeting summarization is about distilling multi-speaker dialogue with distributed information @xcite . prior work treats this as a reduction problem, aiming to condense dialogue using llm prompting @xcite @xcite , role vectors @xcite , hierarchical encoding @xcite , or self-refinement @xcite . these methods improve coherence but often struggle with understanding the meeting's content @xcite as they do not reconstruct the underlying meaning explicitly before summary generation. in contrast, frame reframes summarization as an enrichment task by first extracting and grouping facts from the transcript, planning a high-relevance summary, and enriching the outline to form a summary. fact extraction transforms text into selfcontained, verifiable units @xcite , increasingly used for claim extraction and fact verification @xcite @xcite . prior works on fact extraction in dialogue summarization either enrich utterances with extracted factual statements to reduce hallucinations @xcite , or target specific fact types (e.g., action items) via classification and neighborhood-based rephrasing @xcite . these approaches remain superficial, lacking a general, structured fact representation. typical fact representations may discard salient discourse cues or miss global dependencies @xcite . we address this with statement-context tuples, a structured fact representation pairing claims to global context. unlike others, our representation retains interpretability, allowing for better content comprehension (see appendix g.1). personalization is about adapting summaries to reader expectations @xcite . recent approaches zero-shot llms to align content with a reader's profile @xcite or model participants as graph nodes to extract personalized views @xcite . these approaches can surface relevant points but lack consistency and are prone to reader perspective hallucination @xcite . human-in-theloop systems @xcite ) mitigate this with feedback, but remain laborious, costly, and time-consuming. we approach robust salience detection through our scope reason-out-loud protocol, inspired by cognitive science @xcite and think-outloud protocols observed in human summarizers @xcite . scope guides the llm to answer a questionnaire to build an explicit trace of the reader's intent, expertise, and goals before selecting salient facts. scope outperforms established approaches (see appendix i.2) and works zero-shot, making it scalable and generalizable. ## methodology overview. unlike sequence-to-sequence approaches that attempt one-hop transcript summarization @xcite , frame handles summarization in four stages as a structured enrichment task, inspired by the human summarization process @xcite : extracting salient information, assessing their relevance, organizing them thematically, and synthesizing a coherent narrative. for personalization, we introduce scope, a structured reason-out-loud protocol that enforces generating a reasoning trace to ground salience detection. figure 1 illustrates the complete frame framework and how scope integrates. ## interlude: p-mesa personalization in meeting summarization is gaining traction @xcite , but the field lacks a way to evaluate whether a summary serves a reader's preferences. reference-based metrics like rouge @xcite and bertscore (zhang et al., 2020) require gold summaries, which are typically unavailable in personalized forms. reference-free metrics such as mesa @xcite ) assess general quality aspects (e.g., coreference, irrelevance) but do not judge alignment to reader expectations. recent general personalization metrics like egises @xcite and perseval @xcite are designed to rank-order responses, but not to judge whether a single summary is helpful for a specific user. as a result, researchers have to choose between expensive human evaluations and metrics that overlook reader-centric dimensions, stalling progress in personalized summarization. we introduce p-mesa (personalized -meeting summary assessor), a multi-dimensional, reference-free evaluation framework designed to test whether a single summary satisfies a single target reader. p-mesa scores summaries across seven personalization dimensions motivated from a 50-paper literature review (section 4.1): factuality, completeness, relevance, goal alignment, priority structuring, knowledge-level fit, and contextual framing. definitions are given in appendix j. each dimension covers a distinct personalization characteristic. p-mesa is powered by an llm evaluator (gpt) that receives a reader profile containing role, knowledge level, goals, and interests. this evaluator uses this context information to rate summaries on each dimension on a 5-point likert scale, with higher scores indicating higher impact. ## experiments we evaluate the effectiveness of frame and scope in both general and personalized abstractive meeting summarization. our experiments are aimed at answering two questions: (1) what impact does fact extraction and treating summarization as an enrichment task have on summary quality? (2) how does our guided, structured reason-out-loud protocol compare to prompt-only personalization? ## final considerations we introduced frame, a fact-based pipeline treating abstractive meeting summarization as an enrichment task. frame tackled the core challenges of meetings, i.e., information distribution, contextual dependencies, and salience ambiguity, while reducing hallucinations (3→1), irrelevance (2→1), and repetition (3→1). to handle personalization, we introduced scope. scope builds on a reason-out-loud protocol, guiding a model through nine questions to construct an interpretation trace of the target reader and content before filtering. this improved relevance (3→1), alignment with user goals (3→2), and knowledge level fit (2→1). we further contributed p-mesa, an llm-based personalization quality metric, which assesses the reader fit across seven criteria, offering interpretable scores and strong alignment with human judgment (ρ ≥70%). our contributions advance multiple research directions in summarization, from fact-level content control enabling fine-grained adaptability for crossdocument summarization, to robust salience detection through structured extraction and verification, to reference-free evaluation of reader-specific appropriateness. by releasing frame, scope, and p-mesa as open-source, we provide a powerful toolkit that researchers can extend to multilingual applications, multi-source integration, and domains beyond meeting contexts. this work addresses a persistent gap in summarization approaches that lack the ability to comprehend content before summarization, fostering advancements in summarization and personalization.