Text Summarization on Amazon Food Reviews using TextRank
Yuen Kei Khor, Chi Wee Tan, Tong Ming Lim · 2021
Text summarization is a technique to create a summary by shortening the length of text but keep the key information. There are two main approaches to summarize the text which are abstractive summarization and extractive summarization. This study is aimed to extract the most important top 5 reviews which can summarize the overall reviews of certain product in Amazon fine food reviews. TextRank algorithm which is one of the extractive summarization approaches is used to perform text summarization automatically. GloVe pre-trained word embedding model with 100 dimensions is used to map each word from the reviews to vector representation. Besides, PageRank algorithm is applied to compute the sentence rankings scores to determine how important and relevant of the sentences can be the representatives of summary. Top 5 reviews with the highest sentence ranking scores are extracted to be the summary and further discussed the customer perception on the product based on the summary generated. The final summary shows that Amazon customer reviews tend to positive for certain food brand. Keywords: Text Summarization, Extractive Summarization, TextRank