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dc.contributor.advisorRandall, Todd
dc.contributor.authorKhosravani, Mohammad
dc.date.accessioned2024-05-15T16:50:54Z
dc.date.available2024-05-15T16:50:54Z
dc.date.created2024
dc.date.issued2024
dc.identifier.urihttps://knowledgecommons.lakeheadu.ca/handle/2453/5295
dc.description.abstractIn the current era of mass digital information, the need for effective argument summarization has become paramount. This thesis explores the domain of argument summarization, focusing on the development of techniques and evaluation metrics to improve the quality of summarization models. The study first investigates the task of key point analysis, and the challenges associated with previous approaches to it, emphasizing the significance of coverage of the summary. To address these challenges, we propose a novel clustering-based framework that leverages the inherent semantics of arguments to identify and group similar arguments. The proposed approach is evaluated on the benchmark dataset and compared with previous state-of-the-art methods, demonstrating its effectiveness. In addition to the proposed framework, this thesis also presents an analysis of the previous evaluation metric for argument summarization. Commonly used metric, ROUGE is evaluated, revealing its limitation in capturing the nuanced aspects of argument quality. To this end, we introduce new evaluation metrics and methods that consider the coverage and redundancy of the generated summaries, providing more accurate and informative assessments of summarization models. We further show that our evaluation metric has a better correlation with actual summary quality, whereas previous metrics fail to capture this correlation.en_US
dc.language.isoen_USen_US
dc.titleArgument summarization: enhancing summary generation and evaluation metricsen_US
dc.typeThesisen_US
etd.degree.nameMaster of Scienceen_US
etd.degree.levelMasteren_US
etd.degree.disciplineComputer Scienceen_US
etd.degree.grantorLakehead Universityen_US


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