AutoScraper: A Progressive Understanding Web Agent for Web Scraper Generation

• URL: http://arxiv.org/abs/2404.12753v2
• Date: Thu, 26 Sep 2024 09:17:10 GMT
• Title: AutoScraper: A Progressive Understanding Web Agent for Web Scraper Generation
• Authors: Wenhao Huang, Zhouhong Gu, Chenghao Peng, Zhixu Li, Jiaqing Liang, Yanghua Xiao, Liqian Wen, Zulong Chen,
• Abstract summary: Web scraping is a powerful technique that extracts data from websites, enabling automated data collection, enhancing data analysis capabilities, and minimizing manual data entry efforts. Existing methods, wrappers-based methods suffer from limited adaptability and scalability when faced with a new website. We introduce the paradigm of generating web scrapers with large language models (LLMs) and propose AutoScraper, a two-stage framework that can handle diverse and changing web environments more efficiently.
• Score: 54.17246674188208
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Web scraping is a powerful technique that extracts data from websites, enabling automated data collection, enhancing data analysis capabilities, and minimizing manual data entry efforts. Existing methods, wrappers-based methods suffer from limited adaptability and scalability when faced with a new website, while language agents, empowered by large language models (LLMs), exhibit poor reusability in diverse web environments. In this work, we introduce the paradigm of generating web scrapers with LLMs and propose AutoScraper, a two-stage framework that can handle diverse and changing web environments more efficiently. AutoScraper leverages the hierarchical structure of HTML and similarity across different web pages for generating web scrapers. Besides, we propose a new executability metric for better measuring the performance of web scraper generation tasks. We conduct comprehensive experiments with multiple LLMs and demonstrate the effectiveness of our framework. Resources of this paper can be found at \url{https://github.com/EZ-hwh/AutoScraper}

Related papers

• Web2Code: A Large-scale Webpage-to-Code Dataset and Evaluation Framework for Multimodal LLMs [112.89665642941814]
  Multimodal large language models (MLLMs) have shown impressive success across modalities such as image, video, and audio. Current MLLMs are surprisingly poor at understanding webpage screenshots and generating their corresponding HTML code. We propose a benchmark consisting of a new large-scale webpage-to-code dataset for instruction tuning.
  arXiv  Detail & Related papers  (2024-06-28T17:59:46Z)
• VisualWebBench: How Far Have Multimodal LLMs Evolved in Web Page Understanding and Grounding? [115.60866817774641]
  Multimodal Large Language models (MLLMs) have shown promise in web-related tasks. evaluating their performance in the web domain remains a challenge due to the lack of comprehensive benchmarks. bench is a multimodal benchmark designed to assess the capabilities of MLLMs across a variety of web tasks.
  arXiv  Detail & Related papers  (2024-04-09T02:29:39Z)
• AutoWebGLM: A Large Language Model-based Web Navigating Agent [33.55199326570078]
  We develop the open AutoWebGLM based on ChatGLM3-6B. Inspired by human browsing patterns, we first design an HTML simplification algorithm to represent webpages. We then employ a hybrid human-AI method to build web browsing data for curriculum training.
  arXiv  Detail & Related papers  (2024-04-04T17:58:40Z)
• On the Multi-turn Instruction Following for Conversational Web Agents [83.51251174629084]
  We introduce a new task of Conversational Web Navigation, which necessitates sophisticated interactions that span multiple turns with both the users and the environment. We propose a novel framework, named self-reflective memory-augmented planning (Self-MAP), which employs memory utilization and self-reflection techniques.
  arXiv  Detail & Related papers  (2024-02-23T02:18:12Z)
• Cleaner Pretraining Corpus Curation with Neural Web Scraping [39.97459187762505]
  This paper presents a simple, fast, and effective Neural web Scraper (NeuScraper) to help extract primary and clean text contents from webpages. Experimental results show that NeuScraper surpasses the baseline scrapers by achieving more than a 20% improvement.
  arXiv  Detail & Related papers  (2024-02-22T16:04:03Z)
• A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis [69.15016747150868]
  We introduce WebAgent, an agent that learns from self-experience to complete tasks on real websites. WebAgent plans ahead by decomposing instructions into canonical sub-instructions, summarizes long HTML documents into task-relevant snippets, and acts on websites. We empirically demonstrate that our modular recipe improves the success on real websites by over 50%, and that HTML-T5 is the best model to solve various HTML understanding tasks.
  arXiv  Detail & Related papers  (2023-07-24T14:56:30Z)
• Understanding HTML with Large Language Models [73.92747433749271]
  Large language models (LLMs) have shown exceptional performance on a variety of natural language tasks. We contribute HTML understanding models (fine-tuned LLMs) and an in-depth analysis of their capabilities under three tasks. We show that LLMs pretrained on standard natural language corpora transfer remarkably well to HTML understanding tasks.
  arXiv  Detail & Related papers  (2022-10-08T07:27:17Z)

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.