A Cost-Efficient Modular Sieve for Extracting Product Information from Company Websites (2024)

Abstract: Extracting product information is crucial for informed business decisions and strategic planning across multiple industries. However, recent methods relying only on large language models (LLMs) are resource-intensive and computationally prohibitive due to website structure differences and numerous non-product pages. To address these challenges, we propose a novel modular method that leverages low-cost classification models to filter out company web pages, significantly reducing computational costs. Our approach consists of three modules: web page crawling, product page classification using efficient machine learning models, and product information extraction using LLMs on classified product pages. We evaluate our method on a new dataset of about 7000 product and non-product web pages, achieving a 6-point improvement in F1-score, 95% reduction in computational time, and 87.5% reduction in cost compared to end-to-end LLMs. Our research demonstrates the effectiveness of our proposed low-cost classification module to identify web pages containing product information, making product information extraction more effective and cost-efficient.

## introduction information (e.g., names and descriptions) about products that a company offers is essential for numerous applications such as product search @xcite , product recommendation @xcite , and product knowledge graph construction @xcite . developing a method for obtaining product information is challenging due to (1) the exponential growth of companies and their web pages, which may or may not contain product information; and (2) an unknown structure of company product pages across different company websites. therefore, it is imperative to develop an automated and cost-efficient method to deal with the ever-increasing number of web pages and also handle non-unified structure of product pages. previous work has primarily focused on processing product information from e-commerce web shop data @xcite @xcite @xcite . however, in combination with different page structures across companies, such methods would fall short if dealing with all web pages on a company website, where these pages may or may not be product pages. moreover, including nonproduct pages into the input of a product information extraction system increases computational costs due to the huge number of web pages to process. another challenge is that these pages might be misleading because they could have a structure similar to product pages and diminish the quality of product information. to the best of our knowledge, there is no full method that collects company web pages from diverse company websites and extracts product information from such pages while balancing computational costs and the quality of the obtained product information. in this paper, we propose a novel full pipeline for extracting product information from all web pages on a company website. in contrast to previous work, our method handles pages that may or may not contain information about products. in particular, we extract the product name and product description presented on a company website. our method consists of three major modules (see figure 1 ): (1) crawling, (2) classification, and (3) extraction. in the crawling module, we scrape the company website to collect web pages. for the classification module, we introduce three models to distinguish between product and non-product web pages. finally, for the extraction module, we instruct a pre-trained llm to find product names and descriptions on product web pages. llms have demonstrated effectiveness in extracting information from unknown structured inputs @xcite . to evaluate the quality and computational cost of our method, we curated a new dataset comprising product and non-product web pages from diverse companies. the product pages are annotated by a product scouting expert. to ensure the robustness of our method, we split the dataset at the company level to include only unseen companies in the test set. our experimental results show that our method outperforms off-the-shelf llms in terms of computational cost efficiency while achieving better quality then its peers. to measure the quality, we use precision, recall, and f1 score to assess whether a method identifies correct product pages. we also use rouge (a recall oriented lexical metric) and bertscore (an advanced semantic similarity metric) to evaluate the correctness of the extracted product names and descriptions. our main contributions are: (i) task: while extracting product information from product pages is known, finding products from heterogeneous web pages across company websites has not been studied. (ii) sieve method: we introduce a modular method to identify product pages from a company website and then use them to extract product information. worth noting that, our novelty in method is the entire pipeline that reduces input space for extraction. (iii) empirical evaluation: we collect and annotate a new dataset that contains a representative cross-section of company websites' product and non-product pages. our experiments demonstrate that our method is effective and cost-efficient compared to an off-the-shelf llm. ## related work product information extraction involves extracting attribute/value pairs (specifications) from product information such as name and description. extraction can be performed using a closed-world assumption with a predefined set of attributes, or an openworld assumption where attributes are unknown @xcite . the open-world assumption is more suitable for extracting product information from unstructured data obtained through crawling. product information extraction approaches can be categorized into four major groups: (1) rulebased methods, (2) sequence tagging and namedentity recognition (ner), (3) extractive question answering, and (4) generative approaches. rulebased methods often use token-matching techniques, such as regular expressions, to extract attribute/value pairs @xcite . these methods lack scalability, as a new rule is required for each new attribute @xcite . the sequence labeling approach often involves constructing a model for each attribute @xcite , which also does not scale and generalize well. to address this, question answering approaches consider each attribute as a question -the task is to identify the attribute value as the answer @xcite . for instance, wang et al., 2020 use a single bert model to encode both the context (product information) and question, which makes the approach scalable and generizable. a drawback is that this approach is not suitable for extracting implicit product information, i.e. one that is not explicitly mentioned in the product text @xcite . this problem is resolved by recent api-based large language models (llms), such as gpt-3.5, used to generate attribute/value pairs based on the product information provided on the product web page @xcite @xcite . we employ a generative approach as the latest state-of-the-art in product information extraction @xcite and focus on generating product name and description from the data available on a product page. ## method we develop a novel method to extract products and their descriptions from company web pages. figure 2 depicts details of our method. ## datasets we collect and annotate two sets of web pages associated with different company websites. ## experiments we evaluate the performance and cost efficiency of our method (see section 3) by empirically addressing the following questions: (q1) for the entire task, our method deals with both product and non-product pages together. how do the effects of classifying web pages and processing only product pages impact the quality and computational cost of product information extraction? (q2) how effective is the sequential classification module in classifying web pages? (q3) to what extent can a zero-shot llm extract product information from a given product page? ## conclusions we introduced a full pipeline to efficiently extract product information from web pages on a company website. this approach is in contrast to previous work where any type of web pages (product vs nonproduct pages) is fed into extraction models, which is inefficient and costly. our method consists of three modules: web page crawling, product page classification, and product information extraction. by introducing a classification module that effectively filters out non-product pages, we achieve cost-efficiency and reduce computational overhead. the classification module improves the qualitative performance of extraction as well. the reason behind this is that the classifier is more effective in filtering out non-product pages compared to the examined pre-trained llm. while being effective, our approach still has two limitations. the method is not optimized for extracting several products per page. also, there are no processes to recognize and merge products that are mentioned several times on different pages. the former needs additional prompting whereas the latter can be addressed by duplicate detection methods. in future, building on the promising initial results, we plan to extend our method to extract technical details from product pages, as llms have often been capable of generating such information as structured output. in addition, regular retraining of classifiers and performance monitoring is needed to keep the high quality of the overall method.