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

Hybrid Approach for Single Text Document Summarization using Statistical and Sentiment Features