Data Science Roundup #49: Text Summarization, Open Data, and Fantasy Maps!

Extractive summarization (finding the most important words in a document and using those as a summary) is easier than abstractive summarization (using different words that convey the overall meaning). Google’s recently-released TF model represents their state-of-the-art work in abstractive summarization, and it’s impressive. Get the code here. Also, read the Fast Forward Labs take on it here.

You have to try this. It’s an interesting concept, but the demo is just neat. Within this post, interactively, you start with randomness and then adjust several parameters, eventually ending up with a beautiful fantasy map. The three cities in my map are already vying for the throne.

Is your data science team high-functioning? Domino Data Lab recently updated Joel Spolsky’s 16-year-old test for software engineering teams and applied it to data science, and the results are worth paying attention to. This is short and sweet.

Facebook recently announced that they will punish Facebook Posts which link to articles using clickbait headlines by limiting their exposure on the News Feed. This article walks through a basic but powerful clustering of headlines with some really fun results.

Kaggle initially released its open dataset functionality in January, and they promised at the time that they’d be releasing more in the future. Well, they recently opened the doors: now everyone can post open datasets to Kaggle, version them, collaborate on them, and analyze them, all in public. Very, very cool. I’m already imagining so many applications…

(Speaking of clickbait headlines..!) This post is a quick-hitter, with reviews of five excellent tutorials you may want to check out: Composing Music With Recurrent Neural Networks, Page dewarping using OpenCV, and…well, just click the link.

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