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Found 11 papers, 3 papers with code
CASPR: Automated Evaluation Metric for Contrastive Summarization
Summarizing comparative opinions about entities (e. g., hotels, phones) from a set of source reviews, often referred to as contrastive summarization, can considerably aid users in decision making.
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.).
VRDU: A Benchmark for Visually-rich Document Understanding
Understanding visually-rich business documents to extract structured data and automate business workflows has been receiving attention both in academia and industry.
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.
Simplified DOM Trees for Transferable Attribute Extraction from the Web
There has been a steady need to precisely extract structured knowledge from the web (i. e. HTML documents).
FreeDOM: A Transferable Neural Architecture for Structured Information Extraction on Web Documents
By combining these stages, FreeDOM is able to generalize to unseen sites after training on a small number of seed sites from that vertical without requiring expensive hand-crafted features over visual renderings of the page.
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.
Active Learning for Skewed Data Sets
Furthermore, there is usually only a small amount of initial training data available when building machine-learned models to solve such problems.
Using Paxos to Build a Scalable, Consistent, and Highly Available Datastore
Compared to an eventually consistent datastore, we show that Spinnaker can be as fast or even faster on reads and only 5% to 10% slower on writes.
Databases Distributed, Parallel, and Cluster Computing
