Exploring sentiments in summarization: SentiTextRank, an Emotional Variant of TextRank
Authors: Md.Murad Hossain, Luca Anselma, Alessandro Mazzei (Università di Torino, 2023)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
A summary that aims to preserve the emotions of the original text can be valuable in specific application scenarios, such as generating meta-reviews in both academic and commercial domains. TextRank is a well-known algorithm for automatic extractive summarization. This work introduces SentiTextRank, an emotional variant of TextRank, designed to enhance extractive summarization for both single-document and multi-document contexts. SentiTextRank incorporates emotions into the summarization process by classifying sentences into the eight emotional categories defined in SenticNet. Preliminary evaluations of SentiTextRank show promising results; in particular, our method generates informative summaries composed of sentences that maintain the emotional content of the original document.

Introduction
Summarization is the process of reducing a larger body of information into a concise and coherent summary that captures the essential points and main ideas. Extractive summarization involves selecting and combining sentences or phrases directly from the source text to form the summary [1] and plays an important role in condensing news articles into concise summaries, allowing readers to quickly grasp the key information. Traditional extractive methods primarily rely on lexical word distance to select important sentences for summarization. In many cases the emotional aspects found in the documents are not considered in summarization, and this can affect how readers engage with and understand the information. Sentiment analysis is the extraction of subjective information from text, encompassing emotions and opinions, and the classification based on the expressed emotions, such as happiness, sadness, anger, fear, or surprise, to capture the overall emotional sentiment [2] and [3]. Sentiment analysis had a huge impact on many applications of NLP in the last years, but there is still space for understanding the details of its implementations [4]. The current study employs SenticNet [3], a multidisciplinary approach to opinion mining that lies at the intersection of affective and common-sense computing. This approach integrates elements from semiotics, psychology, linguistics, and machine learning. Unlike statistical sentiment analysis, Sentic computing focuses on preserving the semantic representation of natural language concepts and sentence structure. The foundation of SenticNet is the Hourglass of Emotions, an emotion categorization model designed to accurately express the affective information present in natural language text. To the best of our knowledge, sentiment generation is an understudied argument in the field of automatic summarization. Despite the advancements in text summarization techniques, there is a gap in research when it comes to considering emotions in the process. However, we believe that there are a number of applications in which the emotions in a summary should correspond to the emotions of the original document(s). For instance, this is the case of meta-reviews in conference management or the summarization of product reviews in the case of e-commerce. For these application domains, it is important to consider emotions to create summaries that truly reflect the essence of the source texts. In this paper, by incorporating sentiment scoring between sentences, we generate summaries that capture the emotional tone and impact of the original text. We believe that exploring this aspect further would lead to more comprehensive and effective text summarization methods. This paper has two main goals. First, we define a new algorithm called SentiTextRank, which is an emotional variant of TextRank [5]. Second, we provide an initial evaluation of SentiTextRank by considering two automatic metrics based on content distance. Note that modern LLMs showed some abilities in summarizing texts by using a specific style, with some limitations in producing a summary that is truly extractive. Moreover, LLMs showed also a big impact from the point of view of the required computational resources. We believe that the work presented in this paper, that requires just few hours to conduct all the experiments, can be seen as a cheap (in many senses) alternative to the use of modern expensive (in many senses) LLMs1. The paper is structured as follows. In Section 2, we define the new SentiTextRank algorithm, in Section 3 we report the result of a first experimental evaluation of the SentiTextRank algorithm and the Section 4 ends the paper pointing out to work in progress.

Conclusion
This paper introduces the SentiTextRank algorithm, which integrates emotions into the extractive summarization process to create more informative and emotionally rich summaries. The experimental results are encouraging with respect to the effectiveness of SentiTextRank in capturing factual information. The ongoing work on SentiTextRank is following different directions. First, we want to design a new version of the algorithm that will not be based on the classification of a sentence in one single prevalent emotion. The idea that we want to develop is to define one single measure that combines both content and emotion similarities. By using this combined measure, we can apply the original TextRank algorithm on the entire set of sentences from the source(s) and obtain one single ranking structure accounting for both content and emotion. Second, we want to conduct more extensive experiments also on datasets from different domains. In particular, we are considering medical applications since the affective component of medical information can represent a relevant biopsychosocial feature [12]. Third, we are aware that automatic metrics not always measure a real quality of the summarized text with respect to human judgment [13, 14]. So, we plan in future to conduct human-based evaluation too.