@inproceedings{adhikary-etal-2025-revieweaver,
title = "{R}evie{W}eaver: Weaving Together Review Insights by Leveraging {LLM}s and Semantic Similarity",
author = "Adhikary, Jiban and
Alqudah, Mohammad and
Udayashankar, Arun Palghat",
editor = "Chen, Weizhu and
Yang, Yi and
Kachuee, Mohammad and
Fu, Xue-Yong",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-industry.36/",
doi = "10.18653/v1/2025.naacl-industry.36",
pages = "431--448",
ISBN = "979-8-89176-194-0",
abstract = "With the rise of online retail, customer reviews have become a critical factor in shaping purchasing decisions. The sheer volume of customer reviews being generated continuously presents a challenge for consumers who must sift through an overwhelming amount of feedback. To address this issue, we introduce RevieWeaver, a novel framework that extracts key product features and provides concise review summaries. Our innovative approach not only scales efficiently to 30 million reviews but also ensures reproducibility and controllability. Moreover, it delivers unbiased and reliable assessments of products that accurately reflect the input reviews."
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<abstract>With the rise of online retail, customer reviews have become a critical factor in shaping purchasing decisions. The sheer volume of customer reviews being generated continuously presents a challenge for consumers who must sift through an overwhelming amount of feedback. To address this issue, we introduce RevieWeaver, a novel framework that extracts key product features and provides concise review summaries. Our innovative approach not only scales efficiently to 30 million reviews but also ensures reproducibility and controllability. Moreover, it delivers unbiased and reliable assessments of products that accurately reflect the input reviews.</abstract>
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%0 Conference Proceedings
%T RevieWeaver: Weaving Together Review Insights by Leveraging LLMs and Semantic Similarity
%A Adhikary, Jiban
%A Alqudah, Mohammad
%A Udayashankar, Arun Palghat
%Y Chen, Weizhu
%Y Yang, Yi
%Y Kachuee, Mohammad
%Y Fu, Xue-Yong
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-194-0
%F adhikary-etal-2025-revieweaver
%X With the rise of online retail, customer reviews have become a critical factor in shaping purchasing decisions. The sheer volume of customer reviews being generated continuously presents a challenge for consumers who must sift through an overwhelming amount of feedback. To address this issue, we introduce RevieWeaver, a novel framework that extracts key product features and provides concise review summaries. Our innovative approach not only scales efficiently to 30 million reviews but also ensures reproducibility and controllability. Moreover, it delivers unbiased and reliable assessments of products that accurately reflect the input reviews.
%R 10.18653/v1/2025.naacl-industry.36
%U https://aclanthology.org/2025.naacl-industry.36/
%U https://doi.org/10.18653/v1/2025.naacl-industry.36
%P 431-448
Markdown (Informal)
[RevieWeaver: Weaving Together Review Insights by Leveraging LLMs and Semantic Similarity](https://aclanthology.org/2025.naacl-industry.36/) (Adhikary et al., NAACL 2025)
ACL