Abstract
We propose a topic segmentation model, CSseg (Conceptual Similarity-segmenter), for debates based on conceptual recurrence and debate consistency metrics. We 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. We implemented the prototype of CSseg by utilizing the Korean TV debate program MBC 100-Minute Debate and analyzed the results by use cases. We 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.
Highlights
Debate is an act of discourse in which debaters with different opinions try to persuade the other party to solve a proposed problem [1]
Based on these research questions, we use quantitative metrics representing properties that affect the internal consistency of debate and conceptual recurrence to calculate the similarity between utterances, and based on these research questions, we propose a userdriven topic segmentation model in which the user adjusts the attributes representing the interactions of debaters
Based on the research question of whether there is a relationship between topic segmentation in debate, conceptual recurrence, and the properties influencing the internal consistency of debate, our purpose is to investigate the effect of topic segmentation in debates using the conceptual similarity of conceptual recurrence and metrics influencing the internal consistency of debate
Summary
Debate is an act of discourse in which debaters with different opinions try to persuade the other party to solve a proposed problem [1]. As an approach to solve this problem, we propose a user-driven topic segmentation model from the perspective of visual analytics that analyzes it through user interaction This is an approach in which the user adjusts the properties representing the interaction of the debaters in a visualization environment of conceptual recurrence to separate transcripts and explore topics. Based on these research questions, we use quantitative metrics representing properties that affect the internal consistency of debate and conceptual recurrence to calculate the similarity between utterances, and based on these research questions, we propose a userdriven topic segmentation model in which the user adjusts the attributes representing the interactions of debaters. The proposed model, CSseg, alleviates the problem of each person judging the topic segmentation points differently in debate and multi-party discourse by allowing the user to adjust the weights of the “similarity cohesion calculation method” and “debate consistency metrics.”. We propose a user-centered topic segmentation method by allowing users to adjust the weights of the “similarity cohesion calculation method” and the “debate consistency metrics” through the interactions of the visual analytics tool
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