Aspect-level news browsing
Souneil Park, Sang-Jeong Lee, Junehwa Song · 2010
Aspect-level news browsing provides readers with a classified view of news articles with different viewpoints. It facilitates active interactions with which readers easily discover and compare diverse existing biased views over a news event. As such, it effectively helps readers understand the event from a plural of viewpoints and formulate their own, more balanced viewpoints free from specific biased views. Realizing aspect-level browsing raises important challenges, mainly due to the lack of semantic knowledge with which to abstract and classify the intended salient aspects of articles. We first demonstrate the feasibility of aspect-level news browsing through user studies. We then deeply look into the news article production process and develop framing cycle-aware clustering. The evaluation results show that the developed method performs classification more accurately than other methods.