| Abstract | We present disputant relation-based method for classifying news articles on contentious issues. |
| Abstract | It performs unsupervised classification on news articles based on disputant relations, and helps readers intuitively view the articles through the opponent-based frame. |
| Background and Related Work | The discourse of contentious issues in news articles show different characteristics from that studied in the sentiment classification tasks. |
| Background and Related Work | For example, a news article can cast a negative light on a government program simply by covering the increase of deficit caused by it. |
| Background and Related Work | News articles of a contentious issue are more diverse than debate articles conveying explicit argument of a specific side. |
| Introduction | However, news articles are frequently biased and fail to fairly deliver conflicting arguments of the issue. |
| Introduction | In this paper, we present disputant relation-based method for classifying news articles on con- |
| Introduction | The method helps readers intuitively view the news articles through the opponent-based frame. |
| Introduction | Summarization has been applied to different genres, such as news articles , scientific articles, and speech domains including broadcast news, meetings, conversations and lectures. |
| Opinion Summarization Methods | To obtain this, we trained a maximum entropy classifier with a bag-of-words model using a combination of data sets from several domains, including movie data (Pang and Lee, 2004), news articles from MPQA corpus (Wilson and Wiebe, 2003), and meeting transcripts from AMI corpus (Wilson, 2008a). |
| Related Work | Previous studies have used various domains, including news articles , scientific articles, web documents, reviews. |