In this paper we discuss manual and automatic evaluations of summaries using data from the Document Understanding Conference 2001 (DUC-2001). We first show the instability of the manual evaluation. Specifically, the low interhuman agreement indicates that more reference summaries are needed. To investigate the feasibility of automated summary evaluation based on the recent BLEU method from machine translation, we use accumulative n-gram overlap scores between system and human summaries. The initial results provide encouraging correlations with human judgments, based on the Spearman rank-order correlation coefficient. However, relative ranking of systems needs to take into account the instability.

Following the recent adoption by the machine translation community of automatic evaluation using the BLEU/NIST scoring process, we conduct an in-depth study of a similar idea for evaluating summaries. The results show that automatic evaluation using unigram cooccurrences between summary pairs correlates surprising well with human evaluations, based on various statistical metrics; while direct application of the BLEU evaluation procedure does not always give good results.

In this paper we describe two new objective automatic evaluation methods for machine translation. The first method is based on longest common subsequence between a candidate translation and a set of reference translations. Longest common subsequence takes into account sentence level structure similarity naturally and identifies longest co-occurring insequence n-grams automatically. The second method relaxes strict n-gram matching to skipbigram matching. Skip-bigram is any pair of words in their sentence order. Skip-bigram cooccurrence statistics measure the overlap of skip-bigrams between a candidate translation and a set of reference translations. The empirical results show that both methods correlate with human judgments very well in both adequacy and fluency.