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

TL;DR

This paper introduces a hybrid extractive summarization method combining statistical and sentiment features, including emotion analysis, to improve the selection of salient sentences in single document summarization.

Contribution

It proposes a novel hybrid model that integrates statistical measures with sentiment analysis for extractive summarization, enhancing sentence relevance detection.

Findings

The hybrid model outperforms traditional statistical methods in ROUGE scores.

Including sentiment features improves the quality of extracted summaries.

The approach demonstrates effectiveness compared to existing systems.

Abstract

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.