Heavy research has been done in recent years on tasks of traditional summarization. However, social context, which is critical in building high-quality social summarizer for web documents, is usually neglected. To address this issue, we propose a novel summarization approach based on social context. In this approach, social summarization is implemented by first employing the tripartite clustering algorithm to simultaneously discover document context and user context for a specified document. Then sentence relationships intra and inter documents plus intended user communities are taken into account to evaluate the significance of each sentence in different context views. Finally, a few sentences with highest overall scores are selected to form the summary. Experimental results demonstrate the effectiveness of the proposed approach and show the superior performance over several baselines.
['Po Hu', 'Cheng Sun', 'Longfei Wu', 'Donghong Ji', 'Chong Teng']
Social Summarization via Automatically Discovered Social Context