A template approach for summarizing restaurant reviews

Yenliang Chen, Chialing Chang, Jeryeu Gan

Research output: Contribution to journalReview articlepeer-review

Abstract

In the era of rapid development of social networks, user reviews of restaurant review websites have grown rapidly. In order to allow users to quickly grasp the key points of review information on review sites, this paper provides an abstractive multi-text summary method that can automatically generate template-based review summaries based on predefined topics and sentiments. In particular, for each predefined topic and each type of sentiment (positive or negative), this study uses the TextRank algorithm to find the most representative sentences to form a summary. This method allows users to quickly grasp the positive and negative opinions of each important aspect of the restaurant. The previous research on generating abstracts from reviews either did not generate abstracts based on topics, or they were based on topics generated by random models. However, the latter method cannot guarantee that the topics generated by the random model are really the topics that the user needs. For a restaurant review, some topics are indispensable. In order to ensure that abstracts can be generated for these essential topics, our method predefines the topics that must be generated, and then generates abstracts for these topics. In the evaluation, this study compared the template method with the Refresh and Gensim systems based on criteria such as informativeness, clarity, usefulness and likes. The results show that the method proposed in this paper is superior to the other two summary methods.

Original languageEnglish
Article number9509431
Pages (from-to)115548-115562
Number of pages15
JournalIEEE Access
Volume9
DOIs
StatePublished - 2021

Keywords

  • Restaurant reviews
  • TextRank
  • sentiment analysis
  • summarization
  • template

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