Search Results for author:
Found 8 papers, 6 papers with code
Don't Say What You Don't Know: Improving the Consistency of Abstractive Summarization by Constraining Beam Search
Based on our findings, we present PINOCCHIO, a new decoding method that improves the consistency of a transformer-based abstractive summarizer by constraining beam search to avoid hallucinations.
VILA: Improving Structured Content Extraction from Scientific PDFs Using Visual Layout Groups
Experiments are conducted on a newly curated evaluation suite, S2-VLUE, that unifies existing automatically-labeled datasets and includes a new dataset of manual annotations covering diverse papers from 19 scientific disciplines.
LayoutParser: A Unified Toolkit for Deep Learning Based Document Image Analysis
Recent advances in document image analysis (DIA) have been primarily driven by the application of neural networks.
PAWLS: PDF Annotation With Labels and Structure
Adobe's Portable Document Format (PDF) is a popular way of distributing view-only documents with a rich visual markup.
OLALA: Object-Level Active Learning for Efficient Document Layout Annotation
Document images often have intricate layout structures, with numerous content regions (e. g. texts, figures, tables) densely arranged on each page.
A Large Dataset of Historical Japanese Documents with Complex Layouts
Deep learning-based approaches for automatic document layout analysis and content extraction have the potential to unlock rich information trapped in historical documents on a large scale.
Generating Object Stamps
We present an algorithm to generate diverse foreground objects and composite them into background images using a GAN architecture.
Information Extraction from Text Regions with Complex Tabular Structure
Recent innovations have improved layout analysis of document images, significantly improving our ability to identify text and non-text regions.
