“Iris.ai was interesting and very effective in what I needed to do, and it plugged in pretty well with my information infrastructure”
In today’s article we will continue on our Tech Deep Dive series. This time we will be talking about automatic summarization. We will compare extractive vs. abstractive summarization, what are the challenges and of course we will explain how our abstractive summarization model works.
This is the 3rd and last part of unraveling the Extract tool, where we will take a closer look at how the entities are being linked to the Output Data Layout and what other information the client receives after the extraction is done.
In this blog post, we will walk you through the process of text parsing, entity extractions and variables linking.
The table data extraction is a multistage process and each of the stages has its own challenges. At Iris.ai, we split table data extraction into three downstream tasks – table location, table caption linking, table structure and data extraction.