  The degree of success in document summarization processes depends on the
performance of the method used in identifying significant sentences in the
documents. The collection of unique words characterizes the major signature of
the document, and forms the basis for Term-Sentence-Matrix (TSM). The Positive
Pointwise Mutual Information, which works well for measuring semantic
similarity in the Term-Sentence-Matrix, is used in our method to assign weights
for each entry in the Term-Sentence-Matrix. The Sentence-Rank-Matrix generated
from this weighted TSM, is then used to extract a summary from the document.
Our experiments show that such a method would outperform most of the existing
methods in producing summaries from large documents.
