  Summarization is a way to represent same information in concise way with
equal sense. This can be categorized in two type Abstractive and Extractive
type. Our work is focused around Extractive summarization. A generic approach
to extractive summarization is to consider sentence as an entity, score each
sentence based on some indicative features to ascertain the quality of sentence
for inclusion in summary. Sort the sentences on the score and consider top n
sentences for summarization. Mostly statistical features have been used for
scoring the sentences. We are proposing a hybrid model for a single text
document summarization. This hybrid model is an extraction based approach,
which is combination of Statistical and semantic technique. The hybrid model
depends on the linear combination of statistical measures : sentence position,
TF-IDF, Aggregate similarity, centroid, and semantic measure. Our idea to
include sentiment analysis for salient sentence extraction is derived from the
concept that emotion plays an important role in communication to effectively
convey any message hence, it can play a vital role in text document
summarization. For comparison we have generated five system summaries Proposed
Work, MEAD system, Microsoft system, OPINOSIS system, and Human generated
summary, and evaluation is done using ROUGE score.
