Exploring sentiments in summarization: SentiTextRank, an Emotional Variant of TextRank

A summary that aims at preserving the emotions of the original text can be interesting in certain application scenarios, such as in the generation of metareviews, both in academic and commercial domains. TextRank is a well-studied algorithm for automatic extractive summarization. This work introduces SentiTextRank, an emotional variant of TextRank, to enhance the extractive technique for both single-document and multi-document summarization. SentiTextRank incorporates emotions into the summarization process by classifying sentences into the eight emotional categories used in SenticNet. The preliminary evaluation of SentiTextRank yields encouraging results. In particular, our method generates informative summaries composed of sentences that preserve the emotional content of the original document.