Search Results for author:
Found 15 papers, 11 papers with code
Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research
Language models have become a critical technology to tackling a wide range of natural language processing tasks, yet many details about how the best-performing language models were developed are not reported.
American Stories: A Large-Scale Structured Text Dataset of Historical U.S. Newspapers
The resulting American Stories dataset provides high quality data that could be used for pre-training a large language model to achieve better understanding of historical English and historical world knowledge.
Are Layout-Infused Language Models Robust to Layout Distribution Shifts? A Case Study with Scientific Documents
Recent work has shown that infusing layout features into language models (LMs) improves processing of visually-rich documents such as scientific papers.
Beyond Summarization: Designing AI Support for Real-World Expository Writing Tasks
In this position paper, we argue that developing AI supports for expository writing has unique and exciting research challenges and can lead to high real-world impacts.
The Semantic Reader Project: Augmenting Scholarly Documents through AI-Powered Interactive Reading Interfaces
Scholarly publications are key to the transfer of knowledge from scholars to others.
The Semantic Scholar Open Data Platform
The volume of scientific output is creating an urgent need for automated tools to help scientists keep up with developments in their field.
Multi-LexSum: Real-World Summaries of Civil Rights Lawsuits at Multiple Granularities
With the advent of large language models, methods for abstractive summarization have made great strides, creating potential for use in applications to aid knowledge workers processing unwieldy document collections.
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.
