Social Summarization via Automatically Discovered Social Context
Po Hu, Cheng Jian Sun, Longfei Wu, Donghong Ji, Chong Teng · 2011
Heavy research has been done in recent years on tasks of traditional summarization. How-ever, social context, which is critical in build-ing 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 ap-proach, social summarization is implemented by first employing the tripartite clustering al-gorithm to simultaneously discover document context and user context for a specified docu-ment. Then sentence relationships intra and in-ter documents plus intended user communities are taken into account to evaluate the signifi-cance of each sentence in different context views. Finally, a few sentences with highest overall scores are selected to form the sum-mary. Experimental results demonstrate the ef-fectiveness of the proposed approach and show the superior performance over several baselines. 1