@inproceedings{guanilo-etal-2025-ec,
title = "e{C}-{T}ab2{T}ext: Aspect-Based Text Generation from e-Commerce Product Tables",
author = "Guanilo, Luis Antonio Gutierrez and
Nayeem, Mir Tafseer and
Alamo, Cristian Jose Lopez Del and
Rafiei, Davood",
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.65/",
doi = "10.18653/v1/2025.naacl-industry.65",
pages = "849--867",
ISBN = "979-8-89176-194-0",
abstract = "Large Language Models (LLMs) have demonstrated exceptional versatility across diverse domains, yet their application in e-commerce remains underexplored due to a lack of domain-specific datasets. To address this gap, we introduce eC-Tab2Text, a novel dataset designed to capture the intricacies of e-commerce, including detailed product attributes and user-specific queries. Leveraging eC-Tab2Text, we focus on text generation from product tables, enabling LLMs to produce high-quality, attribute-specific product reviews from structured tabular data. Fine-tuned models were rigorously evaluated using standard Table2Text metrics, alongside correctness, faithfulness, and fluency assessments. Our results demonstrate substantial improvements in generating contextually accurate reviews, highlighting the transformative potential of tailored datasets and fine-tuning methodologies in optimizing e-commerce workflows. This work highlights the potential of LLMs in e-commerce workflows and the essential role of domain-specific datasets in tailoring them to industry-specific challenges."
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%0 Conference Proceedings
%T eC-Tab2Text: Aspect-Based Text Generation from e-Commerce Product Tables
%A Guanilo, Luis Antonio Gutierrez
%A Nayeem, Mir Tafseer
%A Alamo, Cristian Jose Lopez Del
%A Rafiei, Davood
%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 guanilo-etal-2025-ec
%X Large Language Models (LLMs) have demonstrated exceptional versatility across diverse domains, yet their application in e-commerce remains underexplored due to a lack of domain-specific datasets. To address this gap, we introduce eC-Tab2Text, a novel dataset designed to capture the intricacies of e-commerce, including detailed product attributes and user-specific queries. Leveraging eC-Tab2Text, we focus on text generation from product tables, enabling LLMs to produce high-quality, attribute-specific product reviews from structured tabular data. Fine-tuned models were rigorously evaluated using standard Table2Text metrics, alongside correctness, faithfulness, and fluency assessments. Our results demonstrate substantial improvements in generating contextually accurate reviews, highlighting the transformative potential of tailored datasets and fine-tuning methodologies in optimizing e-commerce workflows. This work highlights the potential of LLMs in e-commerce workflows and the essential role of domain-specific datasets in tailoring them to industry-specific challenges.
%R 10.18653/v1/2025.naacl-industry.65
%U https://aclanthology.org/2025.naacl-industry.65/
%U https://doi.org/10.18653/v1/2025.naacl-industry.65
%P 849-867
Markdown (Informal)
[eC-Tab2Text: Aspect-Based Text Generation from e-Commerce Product Tables](https://aclanthology.org/2025.naacl-industry.65/) (Guanilo et al., NAACL 2025)
ACL
- Luis Antonio Gutierrez Guanilo, Mir Tafseer Nayeem, Cristian Jose Lopez Del Alamo, and Davood Rafiei. 2025. eC-Tab2Text: Aspect-Based Text Generation from e-Commerce Product Tables. In 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), pages 849–867, Albuquerque, New Mexico. Association for Computational Linguistics.