Summary of Autoscraper: a Progressive Understanding Web Agent For Web Scraper Generation, by Wenhao Huang et al.
AutoScraper: A Progressive Understanding Web Agent for Web Scraper Generation
by Wenhao Huang, Zhouhong Gu, Chenghao Peng, Zhixu Li, Jiaqing Liang, Yanghua Xiao, Liqian Wen, Zulong Chen
First submitted to arxiv on: 19 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paradigm generates web scrapers with large language models (LLMs), addressing limitations of existing wrappers-based methods and language agents. The two-stage AutoScraper framework leverages HTML structure and page similarity to efficiently handle diverse and changing web environments. It also introduces a new executability metric for evaluating web scraper generation performance. Comprehensive experiments with multiple LLMs demonstrate the effectiveness of the framework. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates machines that can collect website data automatically, making it easier to analyze and work with online information. The current methods struggle when facing new websites or changes in how they’re organized. To solve this problem, researchers developed a system called AutoScraper that uses special language models to generate scrapers for different websites. They also came up with a new way to measure how well these scrapers work. By testing their approach on various language models, they showed that it’s effective and can handle many types of online environments. |
