Search Results for author:
Found 5 papers, 1 papers with code
STRUM-LLM: Attributed and Structured Contrastive Summarization
Users often struggle with decision-making between two options (A vs B), as it usually requires time-consuming research across multiple web pages.
An Augmentation Strategy for Visually Rich Documents
Many business workflows require extracting important fields from form-like documents (e. g. bank statements, bills of lading, purchase orders, etc.).
Radically Lower Data-Labeling Costs for Visually Rich Document Extraction Models
A key bottleneck in building automatic extraction models for visually rich documents like invoices is the cost of acquiring the several thousand high-quality labeled documents that are needed to train a model with acceptable accuracy.
Data-Efficient Information Extraction from Form-Like Documents
Automating information extraction from form-like documents at scale is a pressing need due to its potential impact on automating business workflows across many industries like financial services, insurance, and healthcare.
Representation Learning for Information Extraction from Form-like Documents
We propose a novel approach using representation learning for tackling the problem of extracting structured information from form-like document images.
