A Template Approach for Summarizing Restaurant Reviews
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 genera...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9509431/ |
| _version_ | 1818887447734386688 |
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| author | Yenliang Chen Chialing Chang Jeryeu Gan |
| author_facet | Yenliang Chen Chialing Chang Jeryeu Gan |
| author_sort | Yenliang Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T16:37:23Z |
| format | Article |
| id | doaj.art-0ccd3cd7c9cb4cffbc8dcc3b2c72ddec |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T16:37:23Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0ccd3cd7c9cb4cffbc8dcc3b2c72ddec2022-12-21T20:13:55ZengIEEEIEEE Access2169-35362021-01-01911554811556210.1109/ACCESS.2021.31035129509431A Template Approach for Summarizing Restaurant ReviewsYenliang Chen0https://orcid.org/0000-0001-9103-772XChialing Chang1https://orcid.org/0000-0002-1556-6627Jeryeu Gan2Department of Information Management, School of Management, National Central University, Taoyuan, TaiwanDepartment of Information and Library Science, Tamkang University, New Taipei City, TaiwanDepartment of Information Management, School of Management, National Central University, Taoyuan, TaiwanIn 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.https://ieeexplore.ieee.org/document/9509431/Restaurant reviewssentiment analysissummarizationtemplateTextRank |
| spellingShingle | Yenliang Chen Chialing Chang Jeryeu Gan A Template Approach for Summarizing Restaurant Reviews IEEE Access Restaurant reviews sentiment analysis summarization template TextRank |
| title | A Template Approach for Summarizing Restaurant Reviews |
| title_full | A Template Approach for Summarizing Restaurant Reviews |
| title_fullStr | A Template Approach for Summarizing Restaurant Reviews |
| title_full_unstemmed | A Template Approach for Summarizing Restaurant Reviews |
| title_short | A Template Approach for Summarizing Restaurant Reviews |
| title_sort | template approach for summarizing restaurant reviews |
| topic | Restaurant reviews sentiment analysis summarization template TextRank |
| url | https://ieeexplore.ieee.org/document/9509431/ |
| work_keys_str_mv | AT yenliangchen atemplateapproachforsummarizingrestaurantreviews AT chialingchang atemplateapproachforsummarizingrestaurantreviews AT jeryeugan atemplateapproachforsummarizingrestaurantreviews AT yenliangchen templateapproachforsummarizingrestaurantreviews AT chialingchang templateapproachforsummarizingrestaurantreviews AT jeryeugan templateapproachforsummarizingrestaurantreviews |