Topic Segmentation in Debate
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  • Release Date: 2022-03-28
  • topic segmentation
  • debate
  • visual analytics
  • conceptual recurrence
  • natural language processing
  • text mining
Video Introduction

This video is adapted from 10.3390/app12062952

Researchers propose a topic segmentation model, CSseg (Conceptual Similarity-segmenter), for debates based on conceptual recurrence and debate consistency metrics. They research whether the conceptual similarity of conceptual recurrence and debate consistency metrics relate to topic segmentation. Conceptual similarity is a similarity between utterances in conceptual recurrence analysis, and debate consistency metrics represent the internal coherence properties that maintain the debate topic in interactions between participants. Based on the research question, CSseg segments transcripts by applying similarity cohesion methods based on conceptual similarities; the topic segmentation is affected by applying weights to conceptual similarities having debate internal consistency properties, including other-continuity, self-continuity, chains of arguments and counterarguments, and the topic guide of moderator. CSseg provides a user-driven topic segmentation by allowing the user to adjust the weights of the similarity cohesion methods and debate consistency metrics. It takes an approach that alleviates the problem whereby each person judges the topic segments differently in debates and multi-party discourse. Researchers implemented the prototype of CSseg by utilizing the Korean TV debate program MBC 100-Minute Debate and analyzed the results by use cases. Researchers compared CSseg and a previous model LCseg (Lexical Cohesion-segmenter) with the evaluation metrics Pk and WD. CSseg had greater performance than LCseg in debates.

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Ho, J.; Ha, H.; Lee, S.; Lee, K. Topic Segmentation in Debate. Encyclopedia. Available online: https://encyclopedia.pub/video/video_detail/259 (accessed on 22 April 2024).
Ho J, Ha H, Lee S, Lee K. Topic Segmentation in Debate. Encyclopedia. Available at: https://encyclopedia.pub/video/video_detail/259. Accessed April 22, 2024.
Ho, Jaejong, Hyoji Ha, Seok-Won Lee, Kyungwon Lee. "Topic Segmentation in Debate" Encyclopedia, https://encyclopedia.pub/video/video_detail/259 (accessed April 22, 2024).
Ho, J., Ha, H., Lee, S., & Lee, K. (2022, March 28). Topic Segmentation in Debate. In Encyclopedia. https://encyclopedia.pub/video/video_detail/259
Ho, Jaejong, et al. "Topic Segmentation in Debate." Encyclopedia. Web. 28 March, 2022.