Modeling Novelty and Feature Combination using Support Vector Regression for Update Summarization

Summarization is the process of condensing a piece of text while retaining important information. A well composed and coherent summary is the solution for information overload. Sentence extractive summarization system requires different features to rank sentences and then generate summaries. In this paper we provide a detailed analysis about effect of various features in context of update summarization. We adapt a machine learning algorithm for combining features while scoring a sentence. Further, we propose a new feature that can effectively capture novelty along with relevancy of a sentence in a topic. Evaluation results show that our summmarizer is able to surpass top performing systems participated at Text analysis conference 2008. Gap between oracle summaries and state of art summaries is analyzed to depict the scope of improvement in sentence extractive summarization.

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