That aside, summarization is a relatively old field of study (in CS) and there is tons of good information to read and even many free libraries. My suggestion would be to try out an unsupervised learning algorithm such as LDA. You still need training data, but you don't need to label it with categories. The downside is that you will have no control over what it learns. Still, classic examples of LDA involve classifying news sources.
For standard linear classification, understanding Bayes is important, but for actual implementation look at something like liblinear and use logistic regression with regularization. The difference between Bayes and LR is that Bayes optimizes learning the underlying probability distribution while LR optimizes "getting the classification right", otherwise known as optimizing the expectation. Regularization controls for overfitting and there can be a big difference between the type you use (L1 vs L2) and the settings. Dont' make the mistake of treating it as a minor tweak.
Thank you for your comment. I will look into the stuff you mentioned! This is all relatively new to me!
Also agree with LDA, I've recently been using it for some unsupervised text classification and the topics it generates are impressively sane.
And maybe you should get it. The app looks very impressive. How does your program decide which snippets are the most important? Did you build that technology or license/ use somebody else's code? B/c from what I understand, both Summly and Wavii were primarily acquired for their proprietary Natural Language Processing (NLP) tech. If your app is capable of NLP- and you built it from scratch- you likely have something of real value -- and you're probably in demand as an NLP engineer too.
I do wonder, however, if those recent acquisitions had anything to do with a recent court ruling concerning "Fair Use"/ summarization/ news aggregators. You might wanna take a look & make sure your aggregation of others' content is acceptable + truly protected under "Fair Use" principles. Please note, I am not an attorney.
See here for the original link: https://news.ycombinator.com/item?id=5519622
You are on point with that comment! Actually all of the tech is my code (outside of scraping the image and a few packages via npm). To be honest, I worked pretty hard on some of the NLP for sentence parsing. With that said however, I don't think I am in that high of demand as a developer (yet).
In terms of fair use... I am scared shitless. Right now I am not overly worried because I am not a huge site. The way the algorithm is set up is to take 15% or 3 sentences at max, whichever is less. This is something I will have to approach extremely carefully as I move forward.
In terms of this project's future: I doubt it will get acquired. But I really really hope it will help me get a job once I add it to my portfolio. (I am about to graduate in 1 year!).
Thanks for your reply!
This is not to say that there aren't valuable things that can be added to an article's overview...but I think direct quote/passages or summaries thereof are the least interesting things. I'd rather see something like, how many popular users on Twitter (i.e. not spambots) have tweeted this post, and what are the best comments that are not merely tweets of the title? Or, maybe just pull out the best comments from the stories if Disqus has been enabled. These are the kind of things that would be useful auxiliary information before actually reading the article.
Certainly for professional media outlets and therefore true of the mass market most summarizers are trying to hit. But just take most of the best stuff that reaches HN.. Personal blogs, GitHub repos, documentation. Really hard but potentially rewarding stuff to summarize. I currently do this by hand in the email newsletter space but am certainly interested in any advancements that'll save me time one day :-)
So funny that you mentioned github docs, as I am working on something custom for that (my sentence parsing works really bad with code... who would've thought!). You are exactly right on the potential of things that can be summarized. Currently Summary.io only shows sites with images, but the summaries are still being generated! Appreciate the comment. If you have any other ideas on what I could summarize let me know!
Do-people-need-it? No probably not. But I think for me it helps get to the news articles I really want to read. If I read an interesting sentence or two that was extracted, I feel like I am more likely to go on and read the article.
>>And for sites that do it poorly, I would suspect that their articles will have equally poor extractions. You could not be more right. I have been experimenting with an "Entertainment" section. You will not believe how poor some of the extractions are purely based on the writing style.
I don't think that this solution is perfect, but the wheels are turning inside my head on where I should take this.
No really though, I think that because human writing (and just as importantly, HTML layout) is so diverse, even a very good algorithm risks extracting something very banal or redundant. Cross-referencing an article with social data, however, is very low-hanging fruit and at the same time, pretty useful.
For example, take a post like the currently popular "Introduction to Position Based Fluids" (http://physxinfo.com/news/11109/introduction-to-position-bas...)
An extractor is immediately going to have a problem here because most of the meat is in the video...there is some descriptive text, but it doesn't add much more than the title and the meta description ("Position Based Fluids is a way of simulating liquids using Position Based Dynamics (PBD) approach")
However, the top HN comments are insightful...for example:
"The only uncanny valley effect here is that the water doesn't wet anything - everything is perfect Teflon. So it's hard to judge whether the movement of the water is perfectly realistic, because I keep looking at the transitions. If I don't look at surfaces, it seems very convincing."
And since the mechanism is the same, why not extract the top comment from Reddit?
"Wow. Not much else to say but wow."
OK, not that great in this case...but the retrieval work is trivial. More importantly, both of these sites have built in meta data telling you the value of each comment...not just upvotes, but how much discussion it generated...there are plenty of useful ways to slice this data numerically...And the same pattern would apply to Twitter/Facebook...pull in the comments/reactions from users with high social cred.
In other words, why resort to machine-summarization when you have perfectly fine humans to do so, and who are actually (in some cases) adding new insights?
And there's nothing stopping you from applying text-mining to the social data/commentary you've collected...for example, showing only comments that directly refer to the main keywords/proper nouns that are in the article (this will keep your algorithm from collecting comments like: WOW, AWESOME!! that get upvotes merely for being positive).
Anyway, this seems like an easier route with a lot of value...maybe it's so obvious and technically easy that it's not sexy...but I think it would be very useful.