{"paper":{"title":"Rethinking Meeting Effectiveness: A Benchmark and Framework for Temporal Fine-grained Automatic Meeting Effectiveness Evaluation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Meeting effectiveness can be measured as the rate of objective achievement within each topical segment over time.","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Chenhui Chu, Yihang Li","submitted_at":"2026-04-19T04:59:53Z","abstract_excerpt":"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 "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"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.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That meeting objectives can be reliably identified per topical segment and that human or LLM judgments of 'rate of objective achievement' meaningfully capture overall meeting effectiveness.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Introduces the AMI-ME dataset of 2,459 human-annotated segments and an LLM-judge framework that scores meeting effectiveness as the rate of objective achievement within topical segments.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Meeting effectiveness can be measured as the rate of objective achievement within each topical segment over time.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"9f093c75af9051fb8c03fb07c344c745b905dc9dd00960e2cc453ce04aec137a"},"source":{"id":"2604.17260","kind":"arxiv","version":2},"verdict":{"id":"2637d43c-8df4-4bd7-a111-d07e8b1378a6","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T06:05:28.078910Z","strongest_claim":"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.","one_line_summary":"Introduces the AMI-ME dataset of 2,459 human-annotated segments and an LLM-judge framework that scores meeting effectiveness as the rate of objective achievement within topical segments.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That meeting objectives can be reliably identified per topical segment and that human or LLM judgments of 'rate of objective achievement' meaningfully capture overall meeting effectiveness.","pith_extraction_headline":"Meeting effectiveness can be measured as the rate of objective achievement within each topical segment over time."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.17260/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}