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(Suraj) Hello, world! It's Suraj, and we're going to make an app that reads an article of text and creates a one sentence summary out of it using the power of natural language processing. Language is in many ways the seat of intelligence. It's the original communication protocol that we invented to describe all the incredibly complex processes happening in our neocortex. Do you ever feel like you're getting flooded with an increasing amount of articles and links and videos to choose from? As this data grows, the importance of semantic density does as well. How can you say the most important things in the shortest amount of time? Having a generated summary lets you decide whether you want to deep dive further or not. And the better it gets, the more we'll be able to apply it to more complex language, like that in a scientific paper or even an entire book. The future of NLP is a very bright one. Interestingly enough, one of the earliest use cases for machine summarization was by the Canadian government in the early 90s for a weather system they invented called Fog. Instead of sifting through all the meteorological data they had access to manually, they let Fog read it and generate a weather forecast from it on a recurring basis. It had a set textual template and it would fill in the values for the current weather given the data, something like this. It was just an experiment, but they found that sometimes people actually prefer the computer generated forecasts to the human ones, partly because the generated ones use more consistent terminology. A similar approach has been applied in fields with lots of data that needs human-readable summaries, like finance. And in medicine, summarizing a patient's medical data has proven to be a great decision support tool for doctors. Most summarization tools in the past were extractive, they selected an existing subset of words or numbers from some data to create a summary. But you and I do something a little more complex than that. When we summarize, our brain builds an internal semantic representation of what we've just read and from that, we can generate a summary. This is instead an abstractive method and we can do this with deep learning. What can't we do with it? So let's build a tech summarizer that can generate a headline from a short article using Eras. We're going to use this collection of news articles as our training data. We'll convert it to pickle format, which essentially means converting it into a raw byte stream. Pickling is a way of converting a Python...
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