Summary of Infogent: An Agent-based Framework For Web Information Aggregation, by Revanth Gangi Reddy et al.
Infogent: An Agent-Based Framework for Web Information Aggregation
by Revanth Gangi Reddy, Sagnik Mukherjee, Jeonghwan Kim, Zhenhailong Wang, Dilek Hakkani-Tur, Heng Ji
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 A novel modular framework for web information aggregation, called Infogent, is introduced to navigate different websites and gather information for complex queries. The framework consists of three components: Navigator, Extractor, and Aggregator. Unlike existing methods that focus on linear sequence tasks, Infogent handles the more challenging task of aggregating information from various websites. In two distinct settings, Direct API-Driven Access and Interactive Visual Access, experiments show that Infogent outperforms existing multi-agent search frameworks by 7% and information-seeking web agents by 4.3%, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Infogent is a new way to find information on the internet. It helps computers navigate websites and gather information for complex questions. This is different from how people usually search online, where they follow links and read pages. Infogent has three parts: one that decides what website to go to, one that gets the information from the website, and one that puts all the information together. The researchers tested Infogent on two ways of searching: using special tools like Google’s search API or interacting with a browser like we do when searching online. Infogent did better than existing methods in both cases. |
