Update Summarization using Semi-Supervised Learning Based on Hellinger Distance.

Update summarization aims to generate brief summaries of recent documents to capture new information different from earlier documents. In this paper, we propose a new method to generate the sentence similarity graph using a novel similarity measure based on Helliger distance and apply semi-supervised learning on the sentence graph to select the sentences with maximum consistency and minimum redundancy to form the summaries. We use TAC 2011 data to evaluate our proposed method and compare it with existing baselines. The experimental results show the effectiveness of our proposed method.