Summary of Retrieval Augmented Structured Generation: Business Document Information Extraction As Tool Use, by Franz Louis Cesista et al.
Retrieval Augmented Structured Generation: Business Document Information Extraction As Tool Use
by Franz Louis Cesista, Rui Aguiar, Jason Kim, Paolo Acilo
First submitted to arxiv on: 30 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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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 This paper introduces Business Document Information Extraction (BDIE), a critical problem in transforming unstructured information into structured formats usable by downstream systems. The authors highlight two main tasks: Key-Information Extraction (KIE) and Line Items Recognition (LIR). By modeling BDIE as a Tool Use problem, where tools are downstream systems, the paper presents Retrieval Augmented Structured Generation (RASG), a novel framework achieving state-of-the-art results on both KIE and LIR tasks on BDIE benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary BDIE is like trying to make sense of messy documents. The goal is to turn them into something useful for computers to understand. This paper talks about two important parts: getting key information and recognizing line items. It says that BDIE is best thought of as using tools, which are the systems that need this information. The researchers came up with a new way called RASG that does really well on these tasks. |
