Although previous studies have shown that errors occur in texts summarized by extraction based summarizers, no study has investigated how common different types of errors are and how that changes with degree of summarization. We have conducted studies of errors in extraction based single document summaries using 30 texts, summarized to 5 different degrees and tagged for errors by human judges. The results show that the most common errors are absent cohesion or context and various types of broken or missing anaphoric references. The amount of errors is dependent on the degree of summarization where some error types have a linear relation to the degree of summarization and others have U-shaped or cut-off linear relations. These results show that the degree of summarization has to be taken into account to minimize the amount of errors by extraction based summarizers.