Publication

Abstract : People generally turn to websites such as Yelp to find reviews for the food/restaurant before trying it first-hand. However, some reviews are so long and ambiguous that users get confused about whether the review appreciates or disparages the food. Here, websites might want to employ summarization techniques so that the crux of the study is well understood in just a sentence. The process of compressing the information/data while preserving its idea is text summarization. The present work's main objective is to empirically investigate the suitable algorithm for summarizing reviews so that users do not need to spend time reading through the entire study. The performance of the conducted investigations is analyzed with the metrics such as cosine similarity, the score of the bilingual evaluation understudy (precision), and recall-oriented understudy for gisting evaluation analysis. The experiments show that the text summarization technique, LexRank outperforms other methods with the precision and recall values of 0.586 and 0.346, respectively.