Automatic document summarization has become increasingly important due to the quantity of written material generated world-wide.
Generating good quality summaries enables users to cope with larger amounts of information.
English-document summarization is a difficult task. Yet it is not sufficient. Environmental, economic, and other global issues
make it imperative for English speakers to understand how other countries and cultures perceive and react to important events.
CLASSY (Clustering, Linguistics, And Statistics for Summarization Yield) is an automatic, extract-generating, summarization
system that uses linguistic trimming and statistical methods to generate generic or topic(/query)-driven summaries for single
documents or clusters of documents. CLASSY has performed well in the Document Understanding Conference (DUC) evaluations and
the Multi-lingual (Arabic/English) Summarization Evaluations (MSE).
We present a description of CLASSY. We follow this with experiments and results from the MSE evaluations and conclude with
a discussion of on-going work to improve the quality of the summaries–both English-only and multi-lingual–that CLASSY generates.