We investigate one techmque to produce a summary of an original text without requmng zts full semanttc interpretation, but instead relying on a model of the topic progresston m the text derived from lexlcal chains We present a new algonthm to compute lexlcal chains m a text, merging several robust knowledge sources the WordNet thesaurus, a partof-speech tagger and shallow parser for the identification of nominal groups, and a segmentatton algorithm dernved from (Hearst, 1994) Summarization proceeds m three steps the ongmal text is first segmented, lexxcal chmns are constructed, strong chains are ldsnhfied and ssgnzflcant sentences are extracted from the text We present m tins paper empirical results on the tdent~catlon of strong chains and of slgmfieant sentences
REF constructed lexical chains and extracted strong chains in summaries.
Using Lexical Chains For Text Summarization
Workshop On Intelligent Scalable Text Summarization
Computer Science