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Unsupervised Topic Segmentation of Meetings with BERT Embeddings
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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.
Forward citations
Cited by 5 Pith papers
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Rethinking Meeting Effectiveness: A Benchmark and Framework for Temporal Fine-grained Automatic Meeting Effectiveness Evaluation
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.
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Restructure This: Using AI to Restructure Onboarding Documents to Reduce Cognitive Overload
VisDoc uses GenAI to restructure OSS onboarding documentation according to CTML principles, yielding higher task success and lower cognitive load in a small newcomer study.
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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.
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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 ga...
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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.
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