Identifying Argument Components in Online Debates through Directed Graph and Argument-oriented Summarization

Abstract

Identifying argument components has become an important issue of research in argument mining When argument components are identified they can not only be used for stance classification but also can provide reasons for determining an article is supporting or opposing about a specific target Previous research mainly used text classification and summarization techniques to solve this task However by transforming the task to a classification problem not only rely heavily on choosing and using bag-of-words features but also lose the article entity information due to extract the sentences out of the article and treat as an individual training instance In the other hand although summarization techniques handle on entire article and try to figure out which sentence can best represent the core concept of the article in identifying argument components still heavily relies on bag-of-words feature representation and lack of argument-oriented features to concern about argument components characteristics In our study we dive down to the core of the summarization method adjust it Not only makes it based on argument strength to summarize articles and identify argument components but also proposed a directed graph construction approach and embedded extra documents to enhance and expand the short text semantic due to the short and meaningless sentence in the online debates Experiments show that our proposed method outperforms 8% better than those without argument-oriented methods

Date of Award	2019
Original language	English
Supervisor	Hung-Yu Kao (Supervisor)