  Single document summarization generates summary by extracting the
representative sentences from the document. In this paper, we presented a novel
technique for summarization of domain-specific text from a single web document
that uses statistical and linguistic analysis on the text in a reference corpus
and the web document. The proposed summarizer uses the combinational function
of Sentence Weight (SW) and Subject Weight (SuW) to determine the rank of a
sentence, where SW is the function of number of terms (t_n) and number of words
(w_n) in a sentence, and term frequency (t_f) in the corpus and SuW is the
function of t_n and w_n in a subject, and t_f in the corpus. 30 percent of the
ranked sentences are considered to be the summary of the web document. We
generated three web document summaries using our technique and compared each of
them with the summaries developed manually from 16 different human subjects.
Results showed that 68 percent of the summaries produced by our approach
satisfy the manual summaries.
