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arxiv: 2604.17260 · v1 · submitted 2026-04-19 · 💻 cs.CL

Rethinking Meeting Effectiveness: A Benchmark and Framework for Temporal Fine-grained Automatic Meeting Effectiveness Evaluation

Yihang Li , Chenhui Chu This is my paper

Pith reviewed 2026-05-10 06:05 UTC · model grok-4.3

classification 💻 cs.CL
keywords meeting effectivenesstemporal fine-grained evaluationautomatic evaluationLLM judgeAMI-ME datasetobjective achievement ratemulti-party dialogue
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The pith

Meeting effectiveness can be measured as the rate of objective achievement within each topical segment over time.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper aims to replace single coarse-grained post-meeting survey scores with a temporal fine-grained evaluation that breaks meetings into topical segments and scores each on how quickly it advances the overall objectives. This shift would allow organizations to identify which parts of a discussion succeed or fail without depending on manual assessments that are slow, expensive, and hard to repeat. The authors support the approach by releasing a dataset of thousands of human-annotated segments and by building an automatic system that uses an LLM to judge effectiveness relative to the stated goals. Experiments show the system can be applied across business meetings and less structured discussions, including pipelines that start from raw audio.

Core claim

Effectiveness is defined as the rate of objective achievement over time and is assessed for individual topical segments rather than for an entire meeting at once. The AMI-ME dataset supplies 2,459 human-annotated segments drawn from 130 meetings to serve as a meta-evaluation resource. An automatic framework then employs an LLM as a judge to assign effectiveness scores to each segment relative to the meeting's overall objectives, with benchmarks established for generalizability across meeting types and for end-to-end performance from raw speech.

What carries the argument

The rate of objective achievement over time, evaluated automatically by an LLM judge for each topical segment relative to the meeting objectives.

If this is right

  • Parts of a single meeting can be distinguished as effective or ineffective without waiting for a post-meeting survey.
  • Evaluation scales to many meetings because it no longer requires human raters for every discussion.
  • The same framework can be tested on both structured business meetings and unstructured discussions.
  • End-to-end pipelines from raw speech become possible, allowing complete systems to be benchmarked.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Real-time versions of the segment scoring could let participants adjust a discussion while it is still underway.
  • The segmentation method could extend to other multi-party settings such as online team chats or project updates.
  • Productivity studies could test whether meetings optimized for high achievement rates in each segment produce better long-term outcomes.

Load-bearing premise

That meeting objectives can be reliably identified for each topical segment and that judgments of achievement rates meaningfully reflect overall meeting effectiveness.

What would settle it

A direct comparison in which the fine-grained segment scores show no correlation with independent indicators of meeting success such as whether concrete decisions were made or follow-up tasks were completed.

Figures

Figures reproduced from arXiv: 2604.17260 by Chenhui Chu, Yihang Li.

Figure 1
Figure 1. Figure 1: The paradigm of meeting effectiveness evalu [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Statistics of the AMI-ME dataset. (a) Distribution of segment count per meeting. (b) Distribution of [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The automatic evaluation framework. mentation incorporated 1,668 of the 2,109 original boundaries and introduced 661 new ones, omitting many original boundaries to ensure continuity. 5.2 Human Annotations for Effectiveness After segmentation, we collected human annota￾tions for segment effectiveness through a rigorous quality control process. Given the complexity of the task and the quality differences bet… view at source ↗
Figure 4
Figure 4. Figure 4: The annotation interface. the meeting content fully, corpora from specialized domains like research or politics present a signif￾icant challenge due to the extensive background knowledge required. Therefore, we chose the AMI Corpus (Carletta et al., 2005), which is centered around business scenarios. The AMI Corpus is a multimodal dataset com￾prising 100 hours of meeting recordings. It is en￾riched with a … view at source ↗
Figure 5
Figure 5. Figure 5: Ablation studies of the context window size and the meeting objectives. (a) Experiments on Llama3.3- [PITH_FULL_IMAGE:figures/full_fig_p016_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Relationship between segment scores and duration. (a) Human annotation. (b) Prediction of Qwen3-32B [PITH_FULL_IMAGE:figures/full_fig_p017_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The Spearman correlation coefficient between [PITH_FULL_IMAGE:figures/full_fig_p017_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Illustration of how segmentation granularity [PITH_FULL_IMAGE:figures/full_fig_p018_8.png] view at source ↗
read the original abstract

Evaluating meeting effectiveness is crucial for improving organizational productivity. Current approaches rely on post-hoc surveys that yield a single coarse-grained score for an entire meeting. The reliance on manual assessment is inherently limited in scalability, cost, and reproducibility. Moreover, a single score fails to capture the dynamic nature of collaborative discussions. We propose a new paradigm for evaluating meeting effectiveness centered on novel criteria and temporal fine-grained approach. We define effectiveness as the rate of objective achievement over time and assess it for individual topical segments within a meeting. To support this task, we introduce the AMI Meeting Effectiveness (AMI-ME) dataset, a new meta-evaluation dataset containing 2,459 human-annotated segments from 130 AMI Corpus meetings. We also develop an automatic effectiveness evaluation framework that uses a Large Language Model (LLM) as a judge to score each segment's effectiveness relative to the overall meeting objectives. Through substantial experiments, we establish a comprehensive benchmark for this new task and evaluate the framework's generalizability across distinct meeting types, ranging from business scenarios to unstructured discussions. Furthermore, we benchmark end-to-end performance starting from raw speech to measure the capabilities of a complete system. Our results validate the framework's effectiveness and provide strong baselines to facilitate future research in meeting analysis and multi-party dialogue. Our dataset and code will be publicly available. The AMI-ME dataset and the Automatic Evaluation Framework are available at: this URL.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper proposes a new paradigm for meeting effectiveness evaluation that shifts from coarse post-hoc surveys to a temporal fine-grained approach, defining effectiveness as the rate of objective achievement over time for individual topical segments. It introduces the AMI-ME dataset containing 2,459 human-annotated segments from 130 AMI Corpus meetings and an LLM-as-judge framework to automatically score segment effectiveness relative to overall meeting objectives. The work reports benchmarks for the task, tests of generalizability across business and unstructured meeting types, and end-to-end evaluation starting from raw speech, with public release of the dataset and code.

Significance. If the central claims hold after addressing validation gaps, the work could meaningfully advance meeting analysis and multi-party dialogue research by providing a scalable, segment-level alternative to manual surveys. The public AMI-ME dataset and LLM-judge framework would serve as useful resources for future benchmarks, and the end-to-end speech-to-effectiveness pipeline addresses practical deployment needs.

major comments (2)
  1. [Dataset construction] The human annotation protocol for the 2,459 segments (described in the dataset construction section) reports no inter-annotator agreement statistics, no details on how topical segments were delimited, and no explicit criteria for identifying per-segment objectives; without these, the reliability of the ground-truth labels that underpin the entire benchmark remains unestablished.
  2. [Evaluation and benchmark] The LLM-judge experiments (in the evaluation and benchmark sections) provide no error bars, confidence intervals, or statistical significance tests on the reported scores; this weakens the claims of framework effectiveness and generalizability across meeting types.
minor comments (1)
  1. [Abstract] The abstract ends with a placeholder 'this URL' for dataset availability; this should be replaced with the actual persistent link.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thorough review and constructive feedback on our paper. We address the major comments point by point below, indicating the revisions we plan to make.

read point-by-point responses

  1. Referee: [Dataset construction] The human annotation protocol for the 2,459 segments (described in the dataset construction section) reports no inter-annotator agreement statistics, no details on how topical segments were delimited, and no explicit criteria for identifying per-segment objectives; without these, the reliability of the ground-truth labels that underpin the entire benchmark remains unestablished.

    Authors: We acknowledge that the manuscript does not provide sufficient details on the annotation process. The referee is correct that this information is necessary to establish the reliability of the AMI-ME dataset. In the revised manuscript, we will expand the dataset construction section to include: (1) inter-annotator agreement statistics computed on a double-annotated subset, (2) a description of the process used to delimit topical segments based on shifts in discussion topics from the transcripts, and (3) explicit criteria used by annotators to identify per-segment objectives derived from the overall meeting objectives. These additions will strengthen the foundation of our benchmark. revision: yes

  2. Referee: [Evaluation and benchmark] The LLM-judge experiments (in the evaluation and benchmark sections) provide no error bars, confidence intervals, or statistical significance tests on the reported scores; this weakens the claims of framework effectiveness and generalizability across meeting types.

    Authors: We agree that including measures of statistical variability and significance would better support our claims regarding the LLM-judge framework's performance and generalizability. In the revised manuscript, we will update the evaluation and benchmark sections to include error bars (e.g., standard deviations across multiple runs or meetings), confidence intervals, and appropriate statistical significance tests (such as paired t-tests or Wilcoxon tests) for comparisons between meeting types and against baselines. This will provide a more rigorous presentation of the results. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper defines meeting effectiveness as the rate of objective achievement over time per topical segment, introduces an independent human-annotated dataset (AMI-ME with 2,459 segments), and evaluates an LLM-judge framework against those annotations. No derivation step reduces by construction to its own inputs, fitted parameters, or self-citation chains; the central claims rest on external human validation and standard benchmarking rather than self-referential loops. This is a self-contained benchmark proposal with no load-bearing circular elements.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on two domain assumptions: that meetings possess identifiable objectives that can be localized to topical segments, and that human ratings of per-segment objective achievement constitute valid ground truth for effectiveness.

axioms (2)
  • domain assumption Meetings possess identifiable objectives that can be localized to topical segments
    This premise enables the temporal fine-grained scoring approach described in the abstract.
  • domain assumption Human annotations of per-segment objective achievement provide reliable ground truth
    The dataset and LLM-judge evaluation are built directly on these annotations.

pith-pipeline@v0.9.0 · 5549 in / 1283 out tokens · 50725 ms · 2026-05-10T06:05:28.078910+00:00 · methodology

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Reference graph

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