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Review Summary Generation in Online Systems: Frameworks for Supervised and Unsupervised Scenarios

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Guojun WangAuthors Info & Claims

ACM Transactions on the Web (TWEB), Volume 15, Issue 3

Article No.: 13, Pages 1 - 33

https://doi.org/10.1145/3448015

Published: 13 May 2021 Publication History

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Abstract

In online systems, including e-commerce platforms, many users resort to the reviews or comments generated by previous consumers for decision making, while their time is limited to deal with many reviews. Therefore, a review summary, which contains all important features in user-generated reviews, is expected. In this article, we study “how to generate a comprehensive review summary from a large number of user-generated reviews.” This can be implemented by text summarization, which mainly has two types of extractive and abstractive approaches. Both of these approaches can deal with both supervised and unsupervised scenarios, but the former may generate redundant and incoherent summaries, while the latter can avoid redundancy but usually can only deal with short sequences. Moreover, both approaches may neglect the sentiment information. To address the above issues, we propose comprehensive Review Summary Generation frameworks to deal with the supervised and unsupervised scenarios. We design two different preprocess models of re-ranking and selecting to identify the important sentences while keeping users’ sentiment in the original reviews. These sentences can be further used to generate review summaries with text summarization methods. Experimental results in seven real-world datasets (Idebate, Rotten Tomatoes Amazon, Yelp, and three unlabelled product review datasets in Amazon) demonstrate that our work performs well in review summary generation. Moreover, the re-ranking and selecting models show different characteristics.

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Cited By

Qi PSun YYao MTao D(2024)KEMoS: A knowledge-enhanced multi-modal summarizing framework for Chinese online meetingsNeural Networks10.1016/j.neunet.2024.106417178(106417)Online publication date: Oct-2024
https://doi.org/10.1016/j.neunet.2024.106417
Hayatin NAlias SHung L(2024)Trends and challenges in sentiment summarization: a systematic review of aspect extraction techniquesKnowledge and Information Systems10.1007/s10115-024-02075-w66:7(3671-3717)Online publication date: 9-May-2024
https://dl.acm.org/doi/10.1007/s10115-024-02075-w
Wu BMeng ZLiang S(2023)Dynamic Bayesian Contrastive Predictive Coding Model for Personalized Product SearchACM Transactions on the Web10.1145/360922517:4(1-31)Online publication date: 10-Oct-2023
https://dl.acm.org/doi/10.1145/3609225
Show More Cited By

Index Terms

Review Summary Generation in Online Systems: Frameworks for Supervised and Unsupervised Scenarios
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Natural language generation
2. Information systems
  1. World Wide Web
    1. Web applications
      1. Electronic commerce
        Online shopping

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cover image ACM Transactions on the Web

ACM Transactions on the Web Volume 15, Issue 3

August 2021

162 pages

ISSN:1559-1131

EISSN:1559-114X

DOI:10.1145/3462273

Editor:
Brian D. Davison
Lehigh University, USA

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Copyright © 2021 Association for Computing Machinery.

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Publication History

Published: 13 May 2021

Accepted: 01 January 2021

Revised: 01 November 2020

Received: 01 May 2019

Published in TWEB Volume 15, Issue 3

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Cited By

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https://doi.org/10.1016/j.neunet.2024.106417
Hayatin NAlias SHung L(2024)Trends and challenges in sentiment summarization: a systematic review of aspect extraction techniquesKnowledge and Information Systems10.1007/s10115-024-02075-w66:7(3671-3717)Online publication date: 9-May-2024
https://dl.acm.org/doi/10.1007/s10115-024-02075-w
Wu BMeng ZLiang S(2023)Dynamic Bayesian Contrastive Predictive Coding Model for Personalized Product SearchACM Transactions on the Web10.1145/360922517:4(1-31)Online publication date: 10-Oct-2023
https://dl.acm.org/doi/10.1145/3609225
Chen JJiang WWu JLi KLi K(2023)Dynamic Personalized POI Sequence Recommendation with Fine-Grained ContextsACM Transactions on Internet Technology10.1145/358368723:2(1-28)Online publication date: 19-May-2023
https://dl.acm.org/doi/10.1145/3583687
Al-Murshidawy MAl-Shamma O(2023)Implementing a Modified Low-complexity U-Net Model for Segmenting Brain Tumors2023 Al-Sadiq International Conference on Communication and Information Technology (AICCIT)10.1109/AICCIT57614.2023.10218045(176-180)Online publication date: 4-Jul-2023
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He JJiang WChen GLe YDing X(2022)Enhancing N-Gram Based Metrics with Semantics for Better Evaluation of Abstractive Text SummarizationJournal of Computer Science and Technology10.1007/s11390-022-2125-637:5(1118-1133)Online publication date: 1-Oct-2022
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Shao MJiang WWu JShi YYum TZhang J(2022)Improving Friend Recommendation for Online Learning with Fine-Grained Evolving InterestJournal of Computer Science and Technology10.1007/s11390-021-2124-z37:6(1444-1463)Online publication date: 30-Nov-2022
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Ramakrishnan SBabu L(2022)A Study on Deep Learning Frameworks for Opinion SummarizationProceedings of Emerging Trends and Technologies on Intelligent Systems10.1007/978-981-19-4182-5_18(217-230)Online publication date: 16-Nov-2022
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