Assessing the effect of inconsistent assessors on summarization evaluation

We investigate the consistency of human assessors involved in summarization evaluation to understand its effect on system ranking and automatic evaluation techniques. Using Text Analysis Conference data, we measure annotator consistency based on human scoring of summaries for Responsiveness, Readability, and Pyramid scoring. We identify inconsistencies in the data and measure to what extent these inconsistencies affect the ranking of automatic summarization systems. Finally, we examine the stability of automatic metrics (ROUGE and CLASSY) with respect to the inconsistent assessments.