We investigate one technique to produce a summary of an original text without requiring its full semantic interpretation, but instead relying on a model of the topic progression in the text derived from lexical chains. We present a new algorithm to compute lexical chains in a text, merging several robust knowledge sources: the WordNet thesaurus, a part-of-speech tagger, shallow parser for the identification of nominal groups, and a segmentation algorithm. Summarization proceeds in four steps: the original text is segmented, lexical chains are constructed, strong chains are identified and significant sentences are extracted. We present in this paper empirical results on the identification of strong chains and of significant sentences. Preliminary results indicate that quality indicative summaries are produced. Pending problems are identified. Plans to address these short-comings are briefly presented. 1 Introduction Summarization is the process of condensing a source...
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