This paper contains the results for the FastSum system and a simple baseline system for the TAC 2009 main taskupdate summarization -. For the pilot task of Automatically Evaluating Summaries of Peers (AESOP), we present two novel metrics. The first metric called ContextChain is an extension of a recently proposed metric AutoSummENG that is based on comparing n-gram graphs of the model summaries and the automatically generated summaries. Our modification of the generated n-gram graphs is based

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... co-reference chains extracted from the summaries. The ngram graph is then generated from the context information of these referents. Our second metric called Fractional Conditional Compressibility of Models (FraCC) is based on the Burrows-Wheeler compression algorithm. For this evaluation metric, we use an estimate of the conditional "compressibility" of the model summaries given the system summary. The conditional compressibility is defined as the increase in the compressibility of the model summary when the system summary has been observed. In addition to presenting our two new approaches to automatically evaluating summaries, we will introduce two new evaluation measures for automatic metrics called Correlation Recall and Correlation Precision and discuss how they can cast more light on the coverage and the correctness of the evaluation metrics for summarization.