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⏩ 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.
 
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⏩ OUTLINE:
0:00 - Understanding Problem Statement
02:05 - Example of Meeting Transcript
02:42 - Topic Segmentation Method
04:06 - Sentence Representation Methods(RoBERTa and Sentence-BERT)
06:20 - Segmentation Scheme
07:43 - Results

⏩ Paper Title: Unsupervised Topic Segmentation of Meetings with BERT Embeddings
⏩ Paper: https://arxiv.org/abs/2106.12978
⏩ Code: https://github.com/gdamaskinos/unsupervised_topic_segmentation
⏩ Author: Alessandro Solbiati, Kevin Heffernan, Georgios Damaskinos, Shivani Poddar, Shubham Modi, Jacques Cali
⏩ Organisation: Facebook

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I am Prakhar Mishra and this channel is my passion project. I am currently pursuing my MS (by research) in Data Science. I have an industry work-ex of 3 years in the field of Data Science and Machine Learning with a particular focus on Natural Language Processing (NLP).

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Unsupervised Topic Segmentation of Meetings with BERT Embeddings (Research Paper Walkthrough)