I really dig this.
If I wanted to learn to do something similar, where would I start? I looked at the research paper but I fear I might need some prerequisite steps.
If I wanted to learn to do something similar, where would I start? I looked at the research paper but I fear I might need some prerequisite steps.
One of the nice things about them is that they come at the problem from many different angles. Part of the reason summarization has not been particularly productized is because for a long time the standard approach has involved focusing on a narrow domain, training a model, etc. That gets the best results for the local problem, and focus is great for a startup, but that approach is prone to over-fitting, and it is not scalable, or extensible. Ultimately, it has held the whole category back. The solution is probably to take a bunch of concepts from related fields and combine them within the constraint of a scalable framework.
That's why I recommend these papers (beyond the fact that they are relatively approachable): you can almost sense that he's feeling different surfaces of the problem, trying to map texture, and find the right formula for a great general solution.