Extractive summarization on food reviews

Abstract

Text summarization is a technique to summarize the content of a sizeable text but meanwhile, it keeps the key information. Extractive summarization and abstractive summarization are the main techniques for text summarization. TextRank algorithm, an extractive summarization technique is applied to perform automatic text summarization in this study. Furthermore, GloVe pre-trained word embedding model is used to map each word from the reviews to a vector representation. In the end, the PageRank algorithm is applied to rank the sentences based on their sentence ranking scores. The more important and relevant sentences which can be the representatives of a summary will be placed in a higher rank. The objective of our study is to extract the top five reviews with the highest sentence ranking scores which can form a summary to provide a conspectus of a cookies brand in Amazon food reviews. Besides, a detailed description of the implementation is discussed to provide an overview on using TextRank to create a summary. An analysis of the customer perception based on the summary generated is conducted to understand their needs and level of satisfaction. The final summary demonstrates that Amazon customer reviews for certain cookies brand are generally positive.