Text Summarization of Multi-Aspect Comments in Social Networks in Persian Language

Abstract

Now-a-days, there are increasingly huge amount of user generated comments on the web. The user generated comments usually contains useful and essential information reflecting public’s or customers’ opinions. Since the information in the comments could be used for decision making, production or service improvement, and achieving user satisfaction, the systematic analysis of these comments is an essential need in so many domains including e-commerce, production, and social network analysis. However, the analysis of large volume of comments is a difficult and time-consuming task. Therefore, the need for a system which can convert this massive volume of comments to a useful and efficient summary is felt more and more. Text summarization leads to using more resources at higher speeds and getting richer information. According to numerous studies conducted in the field of multi-document summarization, few studies can be found that have been focused on the user generated comments in Persian language. In this paper, we propose a novel approach to summarize huge amount of comments in Persian, which is enough close to a human summarization. Our approach is based on semantic and lexical similarities and uses a graph-based summarization. We also propose a clustering to deal with multiple aspects (subjects) in a corpus of comments. According to the experiments, the summaries extracted by the proposed approach reached an average score of 8.75 out of 10, which improves the state-of-the-art summarizer’s score about 14 percent.