PRAISE: Enhancing Product Descriptions with LLM-Driven Structured Insights

• URL: http://arxiv.org/abs/2506.17314v1
• Date: Wed, 18 Jun 2025 11:23:39 GMT
• Title: PRAISE: Enhancing Product Descriptions with LLM-Driven Structured Insights
• Authors: Adnan Qidwai, Srija Mukhopadhyay, Prerana Khatiwada, Dan Roth, Vivek Gupta,
• Abstract summary: We present PRAISE: Product Review Attribute Insight Structuring Engine, a novel system that uses Large Language Models (LLMs) to automatically extract, compare, and structure insights from customer reviews and seller descriptions.<n>Our demonstration showcases PRAISE's workflow, its effectiveness in generating actionable structured insights from unstructured reviews, and its potential to significantly improve the quality and trustworthiness of e-commerce product catalogs.
• Score: 47.15503716894445
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate and complete product descriptions are crucial for e-commerce, yet seller-provided information often falls short. Customer reviews offer valuable details but are laborious to sift through manually. We present PRAISE: Product Review Attribute Insight Structuring Engine, a novel system that uses Large Language Models (LLMs) to automatically extract, compare, and structure insights from customer reviews and seller descriptions. PRAISE provides users with an intuitive interface to identify missing, contradictory, or partially matching details between these two sources, presenting the discrepancies in a clear, structured format alongside supporting evidence from reviews. This allows sellers to easily enhance their product listings for clarity and persuasiveness, and buyers to better assess product reliability. Our demonstration showcases PRAISE's workflow, its effectiveness in generating actionable structured insights from unstructured reviews, and its potential to significantly improve the quality and trustworthiness of e-commerce product catalogs.

Related papers

• Contextually Aware E-Commerce Product Question Answering using RAG [0.0]
  E-commerce product pages contain a mix of structured specifications, unstructured reviews, and contextual elements like personalized offers or regional variants.<n>We propose a scalable, end-to-end framework for e-commerce Product Question Answering (PQA) using Retrieval Augmented Generation (RAG)<n>Our system leverages conversational history, user profiles, and product attributes to deliver relevant and personalized answers.
  arXiv  Detail & Related papers  (2025-08-04T02:14:07Z)
• CPR: Leveraging LLMs for Topic and Phrase Suggestion to Facilitate Comprehensive Product Reviews [0.5249805590164902]
  This paper presents CPR, a novel methodology to guide users in crafting insightful and well-rounded reviews.<n>Our approach employs a three-stage process: first, we present users with product-specific terms for rating; second, we generate targeted phrase suggestions based on these ratings.<n>We evaluate CPR using text-to-text LLMs, comparing its performance against real-world customer reviews from Walmart.
  arXiv  Detail & Related papers  (2025-04-18T17:11:38Z)
• Decomposed Opinion Summarization with Verified Aspect-Aware Modules [82.38097397662436]
  We propose a domain-agnostic modular approach guided by review aspects.<n>We conduct experiments across datasets representing scientific research, business, and product domains.
  arXiv  Detail & Related papers  (2025-01-27T09:29:55Z)
• Self-Refinement Strategies for LLM-based Product Attribute Value Extraction [51.45146101802871]
  This paper investigates applying two self-refinement techniques to the product attribute value extraction task.<n>The experiments show that both self-refinement techniques fail to significantly improve the extraction performance while substantially increasing processing costs.<n>For scenarios with development data, fine-tuning yields the highest performance, while the ramp-up costs of fine-tuning are balanced out as the amount of product descriptions increases.
  arXiv  Detail & Related papers  (2025-01-02T12:55:27Z)
• EcomEdit: An Automated E-commerce Knowledge Editing Framework for Enhanced Product and Purchase Intention Understanding [42.41707796705922]
  Knowledge Editing (KE) aims to correct and update factual information in Large Language Models (LLMs) to ensure accuracy and relevance without computationally expensive fine-tuning. ECOMEDIT is an automated e-commerce knowledge editing framework tailored for e-commerce-related knowledge and tasks.
  arXiv  Detail & Related papers  (2024-10-18T08:31:22Z)
• SEOpinion: Summarization and Exploration Opinion of E-Commerce Websites [0.0]
  This paper proposes a methodology coined as SEOpinion (Summa-rization and Exploration of Opinions) It provides a summary for the product aspects and spots opinion(s) regarding them, using a combination of templates' information with the customer reviews in two main phases. To test the feasibility of using Deep Learning-based BERT techniques with our approach, we have created a corpus by gathering information from the top five EC websites for laptops.
  arXiv  Detail & Related papers  (2023-12-12T15:45:58Z)
• Leveraging Large Language Models for Enhanced Product Descriptions in eCommerce [6.318353155416729]
  This paper introduces a novel methodology for automating product description generation using the LLAMA 2.0 7B language model. We train the model on a dataset of authentic product descriptions from Walmart, one of the largest eCommerce platforms. Our findings reveal that the system is not only scalable but also significantly reduces the human workload involved in creating product descriptions.
  arXiv  Detail & Related papers  (2023-10-24T00:55:14Z)
• Automated Extraction of Fine-Grained Standardized Product Information from Unstructured Multilingual Web Data [66.21317300595483]
  We show how recent advances in machine learning, combined with a recently published multilingual data set, enable robust product attribute extraction. Our models can reliably predict product attributes across online shops, languages, or both.
  arXiv  Detail & Related papers  (2023-02-23T16:26:11Z)
• Automatic Controllable Product Copywriting for E-Commerce [58.97059802658354]
  We deploy an E-commerce Prefix-based Controllable Copywriting Generation into the JD.com e-commerce recommendation platform. We conduct experiments to validate the effectiveness of the proposed EPCCG. We introduce the deployed architecture which cooperates with the EPCCG into the real-time JD.com e-commerce recommendation platform.
  arXiv  Detail & Related papers  (2022-06-21T04:18:52Z)
• ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest [60.841761065439414]
  At Pinterest, we build a single set of product embeddings called ItemSage to provide relevant recommendations in all shopping use cases. This approach has led to significant improvements in engagement and conversion metrics, while reducing both infrastructure and maintenance cost.
  arXiv  Detail & Related papers  (2022-05-24T02:28:58Z)
• Improving Factual Consistency of Abstractive Summarization on Customer Feedback [5.084731309706487]
  E-commerce stores collect customer feedback to let sellers learn about customer concerns and enhance customer order experience. A concise summary of the feedback can be generated to help sellers better understand the issues causing customer dissatisfaction. Previous state-of-the-art abstractive text summarization models make two major types of factual errors when producing summaries from customer feedback.
  arXiv  Detail & Related papers  (2021-06-30T16:34:36Z)
• Few-Shot Learning for Opinion Summarization [117.70510762845338]
  Opinion summarization is the automatic creation of text reflecting subjective information expressed in multiple documents. In this work, we show that even a handful of summaries is sufficient to bootstrap generation of the summary text. Our approach substantially outperforms previous extractive and abstractive methods in automatic and human evaluation.
  arXiv  Detail & Related papers  (2020-04-30T15:37:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.