CTSUM: extracting more certain summaries for news articles

People often read summaries of news articles in order to get reliable information about an event or a topic. However, the information expressed in news articles is not always certain, and some sentences contain uncertain information about the event. Existing summarization systems do not consider whether a sentence in news articles is certain or not. In this paper, we propose a novel system called CTSUM to incorporate the new factor of information certainty into the summarization task. We first analyze the sentences in news articles and automatically predict the certainty levels of sentences by using the support vector regression method with a few useful features. The predicted certainty scores are then incorporated into a summarization system with a graph-based ranking algorithm. Experimental results on a manually labeled dataset verify the effectiveness of the sentence certainty prediction technique, and experimental results on the DUC2007 dataset shows that our new summarization system cannot only produce summaries with better content quality, but also produce summaries with higher certainty.