Aspect-Based Summarization: An Approach With Different Levels of Details to Explain Recommendations

• Luan Soares de Souza USP
• Marcelo Garcia Manzato USP

Resumo

Recommender systems have become crucial since they appeared, helping users make decisions. Commonly, recommendation algorithms use the historical interaction data between users and items to predict the users’ tastes and suggest new items. However, offering recommendations sometimes is insufficient for the user to make a decision. In this way, the recommendations’ explanation to support the decision-making process has been considered an essential property. The explanations of recommendations can be generated from different resources, such as unstructured data (e.g., users’ reviews), and presented in many ways, such as summarization. However, offering static explanations may not be helpful in several situations. For example, some users familiar with the content may be willing to receive explanations with fewer details than others who are not acquainted with the domain. In this context, we an approach propose to generate summaries with different levels of detail as post-hoc explanations. We used an aspect-based extractive summarization approach with hierarchical clustering of aspects to select sentences from users’ reviews. Then, this hierarchical structure is used to create explanations of recommended items with different lengths, depending on the user’s preferences. Our dynamic explanation system was evaluated against two state-of-art baselines, and the results are promising.

Palavras-chave: hierarchical clustering, multi-level summarization

Referências

Mehdi Allahyari, Seyedamin Pouriyeh, Mehdi Assefi, Saeid Safaei, Elizabeth D. Trippe, Juan B. Gutierrez, and Krys Kochut. 2017. Text Summarization Techniques: A Brief Survey. https://doi.org/10.48550/ARXIV.1707.02268

Reinald Kim Amplayo, Stefanos Angelidis, and Mirella Lapata. 2021. Aspect-Controllable Opinion Summarization. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic, 6578–6593. https://doi.org/10.18653/v1/2021.emnlp-main.528

Stefanos Angelidis, Reinald Kim Amplayo, Yoshihiko Suhara, Xiaolan Wang, and Mirella Lapata. 2021. Extractive Opinion Summarization in Quantized Transformer Spaces. Transactions of the Association for Computational Linguistics 9 (2021), 277–293. https://doi.org/10.1162/tacl_a_00366

Arthur Bražinskas, Mirella Lapata, and Ivan Titov. 2020. Unsupervised Opinion Summarization as Copycat-Review Generation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, 5151–5169. https://doi.org/10.18653/v1/2020.acl-main.461

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, Minneapolis, Minnesota, 4171–4186. https://doi.org/10.18653/v1/N19-1423

Kavita Ganesan, ChengXiang Zhai, and Jiawei Han. 2010. Opinosis: A Graph Based Approach to Abstractive Summarization of Highly Redundant Opinions. In Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010). Coling 2010 Organizing Committee, Beijing, China, 340–348. https://aclanthology.org/C10-1039

Ruining He and Julian McAuley. 2016. Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering(WWW ’16). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 507–517. https://doi.org/10.1145/2872427.2883037

Solomon Kullback and Richard A Leibler. 1951. On information and sufficiency. The annals of mathematical statistics 22, 1 (1951), 79–86.

Chin-Yew Lin. 2004. ROUGE: A Package for Automatic Evaluation of Summaries. In Text Summarization Branches Out. Association for Computational Linguistics, Barcelona, Spain, 74–81. https://aclanthology.org/W04-1013

Christopher Manning, Prabhakar Raghavan, and Hinrich Schütze. 2010. Introduction to information retrieval. Vol. 16. Cambridge university press, Chapter 17, 346–368.

Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton van den Hengel. 2015. Image-Based Recommendations on Styles and Substitutes(SIGIR ’15). Association for Computing Machinery, New York, NY, USA, 43––52. https://doi.org/10.1145/2766462.2767755

Martijn Millecamp, Nyi Nyi Htun, Cristina Conati, and Katrien Verbert. 2019. To Explain or Not to Explain: The Effects of Personal Characteristics When Explaining Music Recommendations. In Proceedings of the 24th International Conference on Intelligent User Interfaces (Marina del Ray, California) (IUI ’19). Association for Computing Machinery, New York, NY, USA, 397–407. https://doi.org/10.1145/3301275.3302313

Khalil Ibrahim Muhammad, Aonghus Lawlor, and Barry Smyth. 2016. A Live-User Study of Opinionated Explanations for Recommender Systems. In Proceedings of the 21st International Conference on Intelligent User Interfaces (Sonoma, California, USA) (IUI ’16). Association for Computing Machinery, New York, NY, USA, 256–260. https://doi.org/10.1145/2856767.2856813

Cataldo Musto, Marco de Gemmis, Pasquale Lops, and Giovanni Semeraro. 2021. Generating Post Hoc Review-Based Natural Language Justifications for Recommender Systems. User Modeling and User-Adapted Interaction 31, 3 (jul 2021), 629–673. https://doi.org/10.1007/s11257-020-09270-8

Cataldo Musto, Pasquale Lops, Marco de Gemmis, and Giovanni Semeraro. 2019. Justifying Recommendations through Aspect-Based Sentiment Analysis of Users Reviews. Association for Computing Machinery, New York, NY, USA, 4–12. https://doi.org/10.1145/3320435.3320457

Arpit Rana and Derek Bridge. 2018. Explanations That Are Intrinsic to Recommendations. In Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization (Singapore, Singapore) (UMAP ’18). Association for Computing Machinery, New York, NY, USA, 187–195. https://doi.org/10.1145/3209219.3209230

Nils Reimers and Iryna Gurevych. 2019. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, 3982–3992. https://doi.org/10.18653/v1/D19-1410

Gaetano Rossiello, Pierpaolo Basile, and Giovanni Semeraro. 2017. Centroid-based Text Summarization through Compositionality of Word Embeddings. In Proceedings of the MultiLing 2017 Workshop on Summarization and Summary Evaluation Across Source Types and Genres. Association for Computational Linguistics, Valencia, Spain, 12–21 https://doi.org/10.18653/v1/W17-1003

Nava Tintarev and Judith Masthoff. 2015. Explaining recommendations: Design and evaluation. In Recommender systems handbook. Springer, 353–382.

André L. Zanon, Luan Souza, Diany Pressato, and Marcelo G. Manzato. 2020. WordRecommender: An Explainable Content-Based Algorithm Based on Sentiment Analysis and Semantic Similarity. In Proceedings of the Brazilian Symposium on Multimedia and the Web (São Luís, Brazil) (WebMedia ’20). Association for Computing Machinery, New York, NY, USA, 181–184. https://doi.org/10.1145/3428658.3431093

Yongfeng Zhang and Xu Chen. 2020. Explainable Recommendation: A Survey and New Perspectives. Foundations and Trends® in Information Retrieval 14, 1 (mar 2020), 1–101. https://doi.org/10.1561/1500000066