Automatic Text Review Summarization of Digital Library System Application using TextRank Algorithm and TF-IDF

The availability of mobile apps of digital library systems facilitates the needs of library visitors and allow the user give review as a user experience. The summarized user experience can be an insightful input for the development of the mobile app. However, the large number of reviews will take a long time to read and summarize. Thus, a technique should be developed that can provide summarization quickly. Text summarization is a natural language processing technique for extracting information and producing simplified versions of texts, and an example of a popular algorithm is TextRank. In some cases, the algorithm is not optimal without proper feature extraction for identifying sentence rank. The purpose of this study is to provide a text summary from the text review using a combination of the TextRank algorithm and Term Frequency-Inverse Document Frequency (TF-IDF). In addition, this study also analyzes feature extraction techniques in presenting the summary. These methods are evaluated using Rouge-1 and Rouge-2. As a result, the top 10 reviews with the highest sentence rank scores were extracted for summarization. Besides, TF-IDF has a better contribution than Bag of Word in presenting text summaries. where it achieved a score of 0.6014 Rouge-1 and 0.6173 Rouge-2.