SCIENCE@home

AutoScraper: A Progressive Understanding Web Agent for Web Scraper Generation

Wenhao Huang,Zhouhong Gu,C.A.I. Peng,Jiaqing Liang,Zhixu Li,Yanghua Xiao,Liqian Wen,Zulong Chen

Published 2024 in Conference on Empirical Methods in Natural Language Processing

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. Our work is now open-source.

PUBLICATION RECORD

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-29 of 29 references · Page 1 of 1

CITED BY

Showing 1-16 of 16 citing papers · Page 1 of 1