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.
|Original language||English GB|
|Title of host publication||Advances in automatic text summarization|
|Editors||Inderjeet Mani, Mark T Maybury|
|Publisher||The MIT Press|
|Number of pages||11|
|State||Published - 1999|