Generating Attractive and Authentic Copywriting from Customer Reviews

• URL: http://arxiv.org/abs/2404.13906v2
• Date: Tue, 14 May 2024 08:05:00 GMT
• Title: Generating Attractive and Authentic Copywriting from Customer Reviews
• Authors: Yu-Xiang Lin, Wei-Yun Ma,
• Abstract summary: We propose to generate copywriting based on customer reviews, as they provide firsthand practical experiences with products. We have developed a sequence-to-sequence framework, enhanced with reinforcement learning, to produce copywriting that is attractive, authentic, and rich in information. Our framework outperforms all existing baseline and zero-shot large language models, including LLaMA-2-chat-7B and GPT-3.5.
• Score: 7.159225692930055
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The goal of product copywriting is to capture the interest of potential buyers by emphasizing the features of products through text descriptions. As e-commerce platforms offer a wide range of services, it's becoming essential to dynamically adjust the styles of these auto-generated descriptions. Typical approaches to copywriting generation often rely solely on specified product attributes, which may result in dull and repetitive content. To tackle this issue, we propose to generate copywriting based on customer reviews, as they provide firsthand practical experiences with products, offering a richer source of information than just product attributes. We have developed a sequence-to-sequence framework, enhanced with reinforcement learning, to produce copywriting that is attractive, authentic, and rich in information. Our framework outperforms all existing baseline and zero-shot large language models, including LLaMA-2-chat-7B and GPT-3.5, in terms of both attractiveness and faithfulness. Furthermore, this work features the use of LLMs for aspect-based summaries collection and argument allure assessment. Experiments demonstrate the effectiveness of using LLMs for marketing domain corpus construction. The code and the dataset is publicly available at: https://github.com/YuXiangLin1234/Copywriting-Generation.

Related papers

• Beyond Retrieval: Generating Narratives in Conversational Recommender Systems [4.912663905306209]
  We introduce a new dataset (REGEN) for natural language generation tasks in conversational recommendations. We establish benchmarks using well-known generative metrics, and perform an automated evaluation of the new dataset using a rater LLM. And to the best of our knowledge, represents the first attempt to analyze the capabilities of LLMs in understanding recommender signals and generating rich narratives.
  arXiv  Detail & Related papers  (2024-10-22T07:53:41Z)
• Exploring Fine-grained Retail Product Discrimination with Zero-shot Object Classification Using Vision-Language Models [50.370043676415875]
  In smart retail applications, the large number of products and their frequent turnover necessitate reliable zero-shot object classification methods. We introduce the MIMEX dataset, comprising 28 distinct product categories. We benchmark the zero-shot object classification performance of state-of-the-art vision-language models (VLMs) on the proposed MIMEX dataset.
  arXiv  Detail & Related papers  (2024-09-23T12:28:40Z)
• FOCUS: Forging Originality through Contrastive Use in Self-Plagiarism for Language Models [38.76912842622624]
  Pre-trained Language Models (PLMs) have shown impressive results in various Natural Language Generation (NLG) tasks. This study introduces a unique "self-plagiarism" contrastive decoding strategy, aimed at boosting the originality of text produced by PLMs.
  arXiv  Detail & Related papers  (2024-06-02T19:17:00Z)
• GCOF: Self-iterative Text Generation for Copywriting Using Large Language Model [6.439245424286433]
  Large language models such as ChatGPT have substantially simplified the generation of marketing copy. We introduce the Genetic Copy Optimization Framework (GCOF) designed to enhance both efficiency and engagememnt of marketing copy creation. Online results indicate that copy produced by our framework achieves an average increase in click-through rate (CTR) of over $50%$.
  arXiv  Detail & Related papers  (2024-02-21T09:59:20Z)
• A Multimodal In-Context Tuning Approach for E-Commerce Product Description Generation [47.70824723223262]
  We propose a new setting for generating product descriptions from images, augmented by marketing keywords. We present a simple and effective Multimodal In-Context Tuning approach, named ModICT, which introduces a similar product sample as the reference. Experiments demonstrate that ModICT significantly improves the accuracy (by up to 3.3% on Rouge-L) and diversity (by up to 9.4% on D-5) of generated results compared to conventional methods.
  arXiv  Detail & Related papers  (2024-02-21T07:38:29Z)
• Exploring Precision and Recall to assess the quality and diversity of LLMs [82.21278402856079]
  We introduce a novel evaluation framework for Large Language Models (LLMs) such as textscLlama-2 and textscMistral. This approach allows for a nuanced assessment of the quality and diversity of generated text without the need for aligned corpora.
  arXiv  Detail & Related papers  (2024-02-16T13:53:26Z)
• Source Attribution for Large Language Model-Generated Data [57.85840382230037]
  It is imperative to be able to perform source attribution by identifying the data provider who contributed to the generation of a synthetic text. We show that this problem can be tackled by watermarking. We propose a source attribution framework that satisfies these key properties due to our algorithmic designs.
  arXiv  Detail & Related papers  (2023-10-01T12:02:57Z)
• Product Information Extraction using ChatGPT [69.12244027050454]
  This paper explores the potential of ChatGPT for extracting attribute/value pairs from product descriptions. Our results show that ChatGPT achieves a performance similar to a pre-trained language model but requires much smaller amounts of training data and computation for fine-tuning.
  arXiv  Detail & Related papers  (2023-06-23T09:30:01Z)
• Automatic Validation of Textual Attribute Values in E-commerce Catalog by Learning with Limited Labeled Data [61.789797281676606]
  We propose a novel meta-learning latent variable approach, called MetaBridge. It can learn transferable knowledge from a subset of categories with limited labeled data. It can capture the uncertainty of never-seen categories with unlabeled data.
  arXiv  Detail & Related papers  (2020-06-15T21:31:05Z)

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.