Generating Attractive and Authentic Copywriting from Customer Reviews (2024)

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 .

## introduction copywriting is essential in e-commerce, as it assists online stores in showcasing and marketing their products to prospective buyers considerably. research has shown that systems capable of automatically generating persuasive copywriting can significantly improve performance metrics on ecommerce platforms @xcite , reflecting the value of the automated copywriting creation solutions. typical methods of copywriting composition tend to focus only on the predefined attributes of a product @xcite @xcite , leading to content that might be monotonous and unengaging. instead, testimonials have been proven to induce greater interest among readers @xcite , indicating the effectiveness of copywriting incorporating diverse and authentic customer use cases. inspired by these viewpoints, our approach emphasizes creating engaging and reliable copywriting from customer reviews. these reviews provide valuable, firsthand insights about the product, thus offering a more detailed and informative perspective than mere product attributes. to boost the persuasiveness of produced narration, our work endeavors to integrate various dimensions such as factual accuracy, allure in sentence composition, and the richness of information in copywriting. achieving these objectives simultaneously in a supervised manner presents significant challenges. in contrast, reinforcement learning (rl) algorithms have demonstrated proficiency in handling complex tasks, particularly in the areas of summarization @xcite ) and text style transfer @xcite . our work represents a synthesis of these two tasks, driving us to leverage rl techniques to attain the goal. we apply rl algorithms with three dedicated but complementary reward models, responsible for attractiveness, faithfulness, and information density, respectively. for attractiveness, we propose to learn a sentence allure evaluator from pairwise comparisons adjudicated by gpt-3.5 2 . our experiments indicate that using the win rate as a metric from these pairwise comparisons to train a simple regression model aligns more closely with ground truth compared to the binary classification model, which is the method typically used in studies focus-ing on learning human preferences @xcite @xcite . besides, we find that focusing solely on attractiveness in sequence generation might compromise the fidelity, which has been observed in attractive headline generation studies @xcite . to address this, we incorporate a textual entailment model to improve the faithfulness of produced copywriting. also, to enrich the information of created copywriting, we employ a natural language inference model fine-tuned on the extensive qnli dataset @xcite , helping to promote the meaningful content and avoid the appearance of gorgeous but meaningless arguments. our experiments demonstrate that our framework surpasses the baseline in performance, effectively balancing attractiveness, faithfulness, and information richness in the produced content. in sum, our contributions are mainly threefold: ## dataset regarding the abstractive summarization benchmark, previous work has predominantly focused on the cnn/daily mail dataset @xcite and the xsum dataset @xcite . these datasets comprise articles on a broad spectrum of topics and lack relevance to business content, making them unsuitable for our purposes. additionally, there are lots of existing datasets comprised of real-world customer reviews. yelp platform provides a large-scale dataset 3 encompassing customer feedback toward a wide range of business entities. amazon platform offers multi-language review collections sourced from their e-commerce platform @xcite . nonetheless, these datasets are primarily composed of reviews without corresponding summaries. the absence of a corpus poses a challenge in producing marketing content. recently, numerous studies show that summaries generated by llms such as gpt-3.5 or llama-2 @xcite , are on par with humanproduced summaries @xcite @xcite . also, @xcite shows the remarkable capabilities of gpt-3.5 to conduct text summarization conditioned on given keywords. built upon these works, we leverage gpt-3.5 to address the lack of corpus. first, we sample 3622 restaurant reviews from yelp dataset and then leverage the off-the-shelf 3 https://www.yelp.com/dataset according to the review, give me an attractive description about the keyword within 30 english words. the steak is good and flavorful. the salad here is fresh and a must-try. they grill their steaks over oak and citrus wood and the smoky flavor is to die for! steak ## methodology as previous work indicates the proficiency of rl algorithms in handling complex tasks @xcite , especially the success of reinforcement learning from human feedback (rlhf) @xcite @xcite , we leverage rl to enhance our model, aiming to align the created copywriting to human preference. a typical pipeline of rlhf comprises 3 steps: (1) supervised fine-tuning, (2) reward modeling, and (3) reinforcement learning. we follow these steps as previous work suggests. ## experiments all experiments are conducted on an nvidia tesla-v100 gpu. we select the model that performs the best on a validation set. the implementation details are listed in appendix a. ## comparisons with human-written copywriting we have taken the following steps to bolster the experiments with some human-written copywriting to address concerns regarding using llms for training data collection and the word length constraints applied to llm baselines. we employ amazon mechanical turk workers to create copywriting samples. specifically, we gather 100 human-written copywriting pieces from testing samples used in section 5.3. to ensure the quality of these human-generated samples, we meticulously filter out 20 significantly inappropriate or off-topic responses. the instructions given to the turkers are as follows: please write an attractive but faithful summary based on the given keyword and customer review. the summary will be used as product copywriting. you should paraphrase the sentence to make it more attractive and attract potential buyers to buy/enjoy this keyword. but it should be faithful to the original review. ## conclusion in this paper, we present an innovative framework with an elegant but effective reward mechanism, designed to automatically generate attractive and aspect steak tampa source review we got diverted to tampa and decided to make the drive to orlando, but first we needed somewhere to eat. wanting to avoid chain restaurants, yelp steered us toward this little local gem! they grill their steaks over oak, and citrus wood and the smoky flavor is to die for! we are big fans of our smoker back home and cook our steaks this way as well. it's rare to find steak cooked over woodsmoke in most places, and this was worth the side trip! the sides were veggies and sweet potatoes. the wines on offer were so reasonably priced -most under $20 a bottle, but a decent selection. they just don't mark them up 5x like most places! bookmarking this place for a return trip next time i am in tampa for sure! gpt-3.5 juicy, tender, and perfectly grilled, this oak and citrus wood-smoked steak delivers a mouthwatering smoky flavor. cooked to perfection, it's a rare gem worth savoring, a true delight for steak enthusiasts. an enchanting city in florida known for its diverse dining options, tampa captivates visitors with its local gems and the tantalizing aroma of woodsmoked steaks ctrlsum mouthwatering, tender, and smoky steaks grilled over oak and citrus wood, bursting with a delectable flavor that will leave you craving for more. vibrant city with diverse culinary options, including a local gem serving smoky steaks and delicious sides. ours juicy, smoky, and grilled over oak and citrus wood, our steaks at this local gem in tampa are truly exceptional. a rare delight cooked over woodsmoke, it's worth the side trip! the sides were veggies and sweet potatoes. vibrant florida city with smoky steaks, affordable wines, delicious veggies and sweet potatoes. a must-visit! in addition to these developments, we have created an unique dataset comprising summaries and corresponding ranking data, which we believe will be valuable for future research in this field. our dataset is built with the help of gpt-3.5 and includes curated content specifically tailored to advancing the capabilities of automatic copywriting systems, demonstrating the effectiveness of using llms for marketing domain dataset construction. through this work, we aspire to expand the potential and effectiveness of e-commerce platforms, offering new avenues for engaging and authentic content generation.