This post lists the various solution patterns that can be applied for document summarization. Document summarization comes with its challenges related to token limitation and chunk sizes. This blog post discusses about the solutions to tackle those challenges.
Journey on how to solve engineering document digitization problem through a proposed method and the end-to-end solution.
This code story describes CSE's work with ZenCity to create a data pipeline on Azure Databricks supported by a CI/CD pipeline on TravisCI. The aim of the collaboration was to create a pipeline capable of processing a stream of social posts, analyzing them, and identifying trends.
Extracting general concepts, rather than specific phrases, from documents and contracts is challenging. It's even more complicated when applied to scanned documents containing handwritten annotations. We describe using object detection and OCR with Azure ML Package for Computer Vision and Cognitive Services API.
