Analysis of Automated Evaluation for Multi-document Summarization Using Content-Based Similarity

We introduce an automated evaluation method based on content similarity, and construct a vector space of words, on which we compute cosine similarity of automated summaries and human summaries. The method is tested on DUC 2005 data, and produces acceptable results, which may avoid some shortcomings of n-gram. We also test the effects of stopwords and stemming.