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Site owner here. A few thoughts: - HN has been pretty hostile towards Summly and how it is "trivial" or "basic" to create an app that summarizes the news. I've

Site owner here. A few thoughts: - HN has been pretty hostile towards Summly and how it is "trivial" or "basic" to create an app that summarizes the news. I've always wanted to try. All of the negativity actually motivated me to think "Why can't I make something similar." - This has been an incredible learning experience for me. - Feedback is more than welcome.

hyperberry 13y ago
Oh, come on now. You can be honest here... you want $30M like Summly & Wavii :) 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 https://news.ycombinator.com/item?id=5519622
- samsnelling 13y ago
  Hyperberry, 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!
  - sharemywin 13y ago
    do you plan on making this an app?
    - samsnelling 13y ago
      Personally my experience is really on the web side of things. I am however looking at the option of doing an HTML5 wrapper around it so it can be published as an application in the stores. Should be an interesting path!
      - sharemywin 13y ago
        I looked at corona and phonegap. both seem like quick ways to get to an app quickly.
danso 13y ago
I think this is a cool project to undertake but yes, I would group myself among those who think Summly was trivial. Not in a technical sense, but in a do-people-need-it-sense? That is, the best news sites are already going to have relevant headlines and good meta-descriptions. What else could even the best extraction algorithm pull out -- from the text -- that would aid in my decision to read the article? In this problem space, more is not better, because the point is to keep things as concise as possible. I argue that this is largely already done by the content editors. And for sites that do it poorly, I would suspect that their articles will have equally poor extractions. 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.
- petercooper 13y ago
  I argue that this is largely already done by the content editors. 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 :-)
  - samsnelling 13y ago
    Peter, 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!
- 13y ago
tensor 13y ago
I think the arguments against summly have more to do with the fact that they didn't have any tech or in-house expertise. They licensed tech from SRI and even had another group build the app itself. 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.
- samsnelling 13y ago
  >> Dont' make the mistake of treating it as a minor tweak. Yes. Exactly. With how rapidly I have been developing this (2 weeks in the making), I think I could pivot it relatively easily. Really however, classification is just an added layer on top of what I already have. Thank you for your comment. I will look into the stuff you mentioned! This is all relatively new to me!
- jmduke 13y ago
  I've read a lot of literature on classification, but nothing about summarization. Do you have any recommended literature/libraries?
  - samsnelling 13y ago
    jmduke, I started with (http://en.wikipedia.org/wiki/Automatic_summarization http://en.wikipedia.org/wiki/Automatic_summarization) specifically the unsupervised keyphrase extraction. In terms of libraries, there aren't a huge amount of small packages out there outside of the monolithic Stanford NLP package (http://nlp.stanford.edu/software/lex-parser.shtml http://nlp.stanford.edu/software/lex-parser.shtml) and such. When I get back to the house I would be glad to share my bookmarks with you if you are interested.

hyperberry 13y ago

Oh, come on now. You can be honest here... you want $30M like Summly & Wavii :) 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 https://news.ycombinator.com/item?id=5519622

samsnelling 13y ago
Hyperberry, 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!
- sharemywin 13y ago
  do you plan on making this an app?
  - samsnelling 13y ago
    Personally my experience is really on the web side of things. I am however looking at the option of doing an HTML5 wrapper around it so it can be published as an application in the stores. Should be an interesting path!
    - sharemywin 13y ago
      I looked at corona and phonegap. both seem like quick ways to get to an app quickly.

danso 13y ago

I think this is a cool project to undertake but yes, I would group myself among those who think Summly was trivial. Not in a technical sense, but in a do-people-need-it-sense? That is, the best news sites are already going to have relevant headlines and good meta-descriptions. What else could even the best extraction algorithm pull out -- from the text -- that would aid in my decision to read the article? In this problem space, more is not better, because the point is to keep things as concise as possible. I argue that this is largely already done by the content editors. And for sites that do it poorly, I would suspect that their articles will have equally poor extractions. 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.

petercooper 13y ago
I argue that this is largely already done by the content editors. 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 :-)
- samsnelling 13y ago
  Peter, 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!
13y ago

tensor 13y ago

I think the arguments against summly have more to do with the fact that they didn't have any tech or in-house expertise. They licensed tech from SRI and even had another group build the app itself. 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.

samsnelling 13y ago
>> Dont' make the mistake of treating it as a minor tweak. Yes. Exactly. With how rapidly I have been developing this (2 weeks in the making), I think I could pivot it relatively easily. Really however, classification is just an added layer on top of what I already have. Thank you for your comment. I will look into the stuff you mentioned! This is all relatively new to me!
jmduke 13y ago
I've read a lot of literature on classification, but nothing about summarization. Do you have any recommended literature/libraries?
- samsnelling 13y ago
  jmduke, I started with (http://en.wikipedia.org/wiki/Automatic_summarization http://en.wikipedia.org/wiki/Automatic_summarization) specifically the unsupervised keyphrase extraction. In terms of libraries, there aren't a huge amount of small packages out there outside of the monolithic Stanford NLP package (http://nlp.stanford.edu/software/lex-parser.shtml http://nlp.stanford.edu/software/lex-parser.shtml) and such. When I get back to the house I would be glad to share my bookmarks with you if you are interested.