e C - T ab2 T ext: Aspect-Based Text Generation from e-Commerce Product Tables (2025)

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.

## ec-tab2text: dataset construction to address the gap in table-to-text generation for user-specific aspects or queries, such as "camera" and "design & display" (as illustrated in figure 1 ), we developed the ec-tab2text dataset. this dataset comprises e-commerce product tables and is designed to facilitate aspect-based text generation by fine-tuning llms on our dataset. the pipeline for creating ec-tab2text is outlined in figure 2 and described in detail below. ## ec-tab2text: models this section outlines the methodology for table serialization and provides details on the selection and fine-tuning of llms using our dataset. table serialization the representation of tabular data in machine learning has been addressed through various serialization techniques, including markdown format, comma-separated values (csv), html @xcite , and latex @xcite . however, for our specific problem involving semi-structured tables with nested structures, we adopt json serialization. this approach effectively addresses two critical needs: (1) representing the nested structures inherent in product tables and (2) enabling query-specific generation and evaluation @xcite . in our ec-tab2text dataset, both input tables and query-specific outputs are serialized using json. the input json captures structured product specifications, while the output json aligns queries (e.g., "design and display" or "battery") as keys and their corresponding generated texts as values. this unified representation facilitates efficient querying and maintains alignment between inputs and outputs, ensuring consistency across the dataset. additional implementation details can be found in appendix d (listing 7 prompt). ## evaluation in this section, we evaluate the performance of the ec-tab2text models described in section 4 along with several closed-source models, including gpt-4o-mini and gemini-1.5-flash. the evaluation follows standard metrics commonly used in table-totext generation, as outlined in @xcite . these metrics include bleu @xcite , the f-1 scores of rouge-1 and rouge-l @xcite , meteor @xcite , and bertscore @xcite , following (akash et al., 2023; column name data type description table dictionary contains structured data with headers and rows. example_id string unique identifier for each dataset example. query string textual description or query related to the dataset. summary string summary or explanation generated in response to the query. row_ids sequence of integers row indices corresponding to the data referenced in the table column. ## discussion and future directions this section highlights the need for better numerical reasoning in table-to-text generation and im-proved evaluation methods. numerical reasoning product tables, with their semi-structured and nested attributes (e.g., battery capacity in mah, display size in inches), demand advanced numerical reasoning to generate meaningful text. models must analyze relationships, such as how battery life depends on capacity and display size, or how display dimensions impact user experience. unlike wikipedia tables @xcite , which focus on factual text generation, our ec-tab2text dataset challenges models to integrate numerical reasoning with qualitative text generation @xcite . this unique focus enables llms to synthesize structured data into nuanced, humanreadable summaries, providing a benchmark for evaluating and improving reasoning capabilities in real-world applications @xcite @xcite . future work could explore pushing the boundaries of llms capabilities in numerical and qualitative reasoning using our dataset. evaluation although we evaluated the correctness, faithfulness, and fluency of the generated text using prometheus 2 @xcite , attribute-specific text evaluation against product tables requires a more nuanced approach. future evaluations could involve extracting attribute-value pairs from the generated text @xcite , verifying their correctness and contextual relevance, and comparing them with the corresponding values in the source tables to enable more fine-grained and precise assessments. ## conclusion this work introduces ec-tab2text, a novel dataset for table-to-text generation in the e-commerce domain, addressing the limitations of existing generalpurpose datasets. by fine-tuning open-source llms, we demonstrate substantial improvements in generating attribute-specific, contextually accurate product reviews. our evaluation highlights the robustness of ec-tab2text, outperforming comparable datasets like qtsumm, and underscores the importance of domain-specific datasets for advancing llm performance in industry-specific applications. this study lays the groundwork for future research in expanding dataset scope, evaluation methodologies, and enhancing numerical reasoning in e-commerce workflows.