基於有向圖與爭論導向摘要的網路辯論之爭論元素辨識

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

辨識觀點在「辯論語意挖掘」的領域中扮演著重要的角色。在任意文章中辨識出論點，不僅可以用在被立場分類做為特徵來源增加辨識的準確性，同時也可以做為一篇文章表明其支持或反對一個目標的理由。在這項研究中，已經有許多方法嘗試解決這一項任務，較廣泛提起的是文字分類方法和自動化摘要的技巧。然而，將辨識觀點轉化成文字分類的問題不僅著重在特徵的選擇和使用，同時也因為將文章中一個一個句子出視為獨立的句子，所以也失去了文章整體的關係；自動化摘要的技巧雖然將文章中的所有句子一起處理評估出其中一句適合做為文章代表的句子，然而現有的一些方法多使用詞袋模型以及通用的自動化摘要方法，不僅沒有表示的特徵過於稀疏，同時亦沒有考量及引入辯論文章的特性。本研究主要在可以保留文章為一體的自動化摘要方法上進行深刻的觀察及調整，使其不僅可以以辯論強度為導向摘要文章，並建構一有向圖來表示這些文章。在這些增強和調整下，不僅讓原本無辯論導向的自動化摘要方法可以更好的排序出辯論的句子。最終，本研究提出的方法可以在辯論文章中識別論點句子的任務上提升8％的辨識準確率。

Identifying argument components has become an important area 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, not only makes it based on argument strength to summarize articles and identify argument components, but also proposed a directed graph construction approach. Experiments show that our proposed method outperforms 8% better than those without argument-oriented methods.