An LLM-as-judge framework scores meeting transcript segments for objective achievement over time, validated on a new 2,459-segment human-annotated dataset from the AMI Corpus.
arXiv preprint arXiv:2106.12978 , year=
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
abstract
Topic segmentation of meetings is the task of dividing multi-person meeting transcripts into topic blocks. Supervised approaches to the problem have proven intractable due to the difficulties in collecting and accurately annotating large datasets. In this paper we show how previous unsupervised topic segmentation methods can be improved using pre-trained neural architectures. We introduce an unsupervised approach based on BERT embeddings that achieves a 15.5% reduction in error rate over existing unsupervised approaches applied to two popular datasets for meeting transcripts.
years
2026 3representative citing papers
CobSeg is a multi-branch architecture for dialogue topic segmentation that separates semantic continuity from lexical transitions, uses boundary informativeness weighting and corpus-derived cues, and reports metric gains on five benchmarks under both gold and induced boundary training.
VisDoc uses a GenAI pipeline grounded in CTML to restructure OSS onboarding docs, with small evaluations showing higher task success and lower cognitive load.
citing papers explorer
-
Rethinking Meeting Effectiveness: A Benchmark and Framework for Temporal Fine-grained Automatic Meeting Effectiveness Evaluation
An LLM-as-judge framework scores meeting transcript segments for objective achievement over time, validated on a new 2,459-segment human-annotated dataset from the AMI Corpus.
-
CobSeg: Coherence Boundary Modeling for Dialogue Topic Segmentation
CobSeg is a multi-branch architecture for dialogue topic segmentation that separates semantic continuity from lexical transitions, uses boundary informativeness weighting and corpus-derived cues, and reports metric gains on five benchmarks under both gold and induced boundary training.
-
Restructure This: Using AI to Restructure Onboarding Documents to Reduce Cognitive Overload
VisDoc uses a GenAI pipeline grounded in CTML to restructure OSS onboarding docs, with small evaluations showing higher task success and lower cognitive load.