W eb Q uality: A Large-scale Multi-modal Web Page Quality Assessment Dataset with Multiple Scoring Dimensions (2025)

Abstract: The assessment of web page quality plays a critical role in a range of downstream applications, yet there is a notable absence of datasets for the evaluation of web page quality. This research presents the pioneering task of web page quality assessment and introduces the first comprehensive, multi-modal Chinese dataset named WebQuality specifically designed for this task. The dataset includes over 65,000 detailed an-notations spanning four sub-dimensions and incorporates elements such as HTML+CSS, text, and visual screenshot, facilitating in-depth modeling and assessment of web page quality. We performed evaluations using a variety of baseline models to demonstrate the complexity of the task. Additionally, we propose Hydra, an integrated multi-modal analysis model, and rigorously assess its performance and limitations through extensive ablation studies. To advance the field of web quality assessment, we offer unrestricted access to our dataset and codebase for the research community, available at https://github.com/incredible-smurf/WebQuality

## introduction as web page serves as carriers of diverse and vast knowledge, selecting superior ones facilitates more effective knowledge acquisition. current research for web page knowledge acquisition predominantly focuses on the relevance of web content @xcite , often neglecting the aspect of web page quality. the presence of low-quality web page data can significantly deteriorate user experiences @xcite in industrial applications and compromise the performance of computational models @xcite in scientific research. identifying and filtering out low-quality content from the vast array of web data, to prioritize high-quality information, becomes an * these authors contributed equally to this work † corresponding authors urgent issue. in response to our survey indicating a lack of open-source web page quality datasets, we introduce the first dataset capable of training a quality model for web page quality assessment. this paper proposes a dataset for web page quality assessment, acknowledging the complexity and multi-dimensionality of the task. the evaluation process is complicated by the diversity of web pages from various sources, requiring distinct evaluation criteria. further complexity arises from the subjective nature of quality assessments and the need for a universally applicable standard. furthermore, as web pages are dynamic and constantly changing, we aim to preserve static web page data to ensure consistency in annotations. against this backdrop, the paper addresses two fundamental challenges in web page quality assessment. q1: how to reasonably evaluate the quality of web pages? ## conclusion in this study, we introduce webquality, a comprehensive chinese multi-modal dataset specifically designed for evaluating web page quality, which comprises html+css, text, and visual screenshot. our objective centers on a holistic assessment of web pages, scrutinizing four specific subdimensions. evaluations with various baseline models on this dataset underscore the intricacies of assessing web page quality. we propose the hydra model, which utilizes html+css, text, and screenshot data for cohesive analysis. despite setting a new benchmark, its limitations highlight the need for further exploration in multi-modal integration. this research contributes to methodologies in web page quality assessment, with future directions aimed at developing versatile models for practical applications such as web data filtering for llm pre-training. template setting please classify the quality of the web page according to the following title, content, and web page quality assessment criteria, and output: good, average, or poor; web page quality assessment criteria: <quality_criteria>; title: <web_title>; content: <web_content>.