5 ms·

Hi everyone I am delighted to share my new project "TLDR This" with you guys. Problem : There's so much content out there but too little time to read. So many

Hi everyone I am delighted to share my new project "TLDR This" with you guys. Problem : There's so much content out there but too little time to read. So many times, it happens that there's a long article that we're interested in, but we don't really feel like scouring through it to extract relevant information from it. Hence, I created "TLDR This" to help you navigate relevant content quickly and easily, without having to read the whole thing. Steps to get cracking - 1. Copy and paste either the URL or the text of the article you'd like summarized. 2. Press the "Process Text" button, and you are good to go. TLDR This also comes with a chrome extension, allowing you to summarize any webpage at the click of a button. How to use the chrome extension? Just click the “tl;dr” button in Chrome's toolbar on a webpage which you'd like summarized and within a few seconds, you'll get the < 5 sentence summary right there. Please let me know if you have any feedback/suggestions. Thanks a lot

valiant-comma 7y ago
Congrats! It worked well on a few sample articles that I tried. Can you provide background on how the text is summarized?
- radhakrsna 7y ago
  Thanks a lot. Sure, first we create a list of all the individual sentences in the article. Then, we use the TextRank algorithm to rank each of the text sentences. Finally, we select the top 5 most representative sentences from the article.
  - dvh 7y ago
    How do you create list of sentences from the article? How do you know where article starts and what is just navigation or fluff.
    - harias 7y ago
      Firefox's reader's view is able to do that. Check out their Github repo here - https://github.com/mozilla/readability https://github.com/mozilla/readability >You need at least one <p> tag around the text, you want to see in Reader View and at least 516 characters in 7 words inside the text. Source - https://stackoverflow.com/questions/30661650/how-does-firefox-reader-view-operate https://stackoverflow.com/questions/30661650/how-does-firefo...
    - radhakrsna 7y ago
      Hi, We used a python library called newspaper to do it.
      - holler 7y ago
        how does this compare to newspaper's built-in summary feature? What is different?
megaremote 7y ago
Some info about it would be great? Do you use an AI model? Do you do semantic analysis? Is it open source? I tried it on a wikipedia article and it didn't work too well.
thatoneuser 7y ago
Unless you make this painfully transparent, a rational person would have to assume some intent of bias. No offense intended - I'm stoked you made this as its one of those tools I've always intended to make myself. But honestly, given the revelations of the last few years - unless we can verify as a community that there isn't bias, we have to assume there is.
josteink 7y ago
> TLDR This also comes with a chrome extension Firefox uses the exact same extension format, and except for non-trivial cases, “porting” an extension to Firefox involves literally no code-changes. Consider publishing your extension for Firefox too. You just need a Mozilla-account to publish it on addons.mozilla.org. That’s pretty much it.
- radhakrsna 7y ago
  Hi, That's a very good suggestion. I didn't know it was that easy to build an extension for Firefox. I will publish it on the Mozilla store as soon as possible. Thank you very much for taking the time to use the app and providing your feedback.
- radhakrsna 7y ago
  Hi! We have added the link for Firefox extension. Please check it out and provide your feedback. Thank you!
nudpiedo 7y ago
Introduce this article: https://johnnyrodgers.is/burnout https://johnnyrodgers.is/burnout Each section subtitle is displayed as a sentence. Could you maybe ML generate a 1 sentence summary for section? I don't think there is an ML which could extract the take aways from each section and make a summary correctly, but that was my original expectation when I saw it first time.
- applecrazy 7y ago
  > I don't think there is an ML which could extract the take aways from each section There's plenty of abstractive and extractive methods for text summarization. In fact, that's what I'm working on right now, is to develop a news article summarization system for longer articles. If you're interested in learning more, there's an excellent survey paper on ArXiv: https://arxiv.org/abs/1812.02303 https://arxiv.org/abs/1812.02303
  - nudpiedo 7y ago
    is there any working demo to play with? I am not into ML and won't read the whole paper... I expect these sort of mechanisms to make some weighting of the morphemes and sentences and replicate the top score ones whereas a human made summary is based on context and semantics given by humans (for example, the relevant part might be only relevant during the current zeitgeist or for some peoples bias)
    - applecrazy 7y ago
      I'm not aware of any working demos. In terms of the weighting, your intuition is correct, there's a way how a machine learning algorithm can learn which tokens (what you call morphemes) are of significance automatically. That's called an attention mechanism. It learns what tokens are important and then passes that into a "context vector" of sorts, which is then passed into the generator network (which is some form of recurrent network). The disadvantage of using only token-based attention is that it's still not enough context for large spans of text (> 1000 tokens), and I'm actually having this issue now in my work. That's where things like applying attention to chunks of text is helpful. I'm still trying to fully understand attention mechanisms, so I hope this comment made sense.

valiant-comma 7y ago

Congrats! It worked well on a few sample articles that I tried. Can you provide background on how the text is summarized?

radhakrsna 7y ago
Thanks a lot. Sure, first we create a list of all the individual sentences in the article. Then, we use the TextRank algorithm to rank each of the text sentences. Finally, we select the top 5 most representative sentences from the article.
- dvh 7y ago
  How do you create list of sentences from the article? How do you know where article starts and what is just navigation or fluff.
  - harias 7y ago
    Firefox's reader's view is able to do that. Check out their Github repo here - https://github.com/mozilla/readability https://github.com/mozilla/readability >You need at least one <p> tag around the text, you want to see in Reader View and at least 516 characters in 7 words inside the text. Source - https://stackoverflow.com/questions/30661650/how-does-firefox-reader-view-operate https://stackoverflow.com/questions/30661650/how-does-firefo...
  - radhakrsna 7y ago
    Hi, We used a python library called newspaper to do it.
    - holler 7y ago
      how does this compare to newspaper's built-in summary feature? What is different?

megaremote 7y ago

Some info about it would be great? Do you use an AI model? Do you do semantic analysis? Is it open source? I tried it on a wikipedia article and it didn't work too well.

thatoneuser 7y ago

Unless you make this painfully transparent, a rational person would have to assume some intent of bias. No offense intended - I'm stoked you made this as its one of those tools I've always intended to make myself. But honestly, given the revelations of the last few years - unless we can verify as a community that there isn't bias, we have to assume there is.

josteink 7y ago

> TLDR This also comes with a chrome extension Firefox uses the exact same extension format, and except for non-trivial cases, “porting” an extension to Firefox involves literally no code-changes. Consider publishing your extension for Firefox too. You just need a Mozilla-account to publish it on addons.mozilla.org. That’s pretty much it.

radhakrsna 7y ago
Hi, That's a very good suggestion. I didn't know it was that easy to build an extension for Firefox. I will publish it on the Mozilla store as soon as possible. Thank you very much for taking the time to use the app and providing your feedback.
radhakrsna 7y ago
Hi! We have added the link for Firefox extension. Please check it out and provide your feedback. Thank you!

nudpiedo 7y ago

Introduce this article: https://johnnyrodgers.is/burnout https://johnnyrodgers.is/burnout Each section subtitle is displayed as a sentence. Could you maybe ML generate a 1 sentence summary for section? I don't think there is an ML which could extract the take aways from each section and make a summary correctly, but that was my original expectation when I saw it first time.

applecrazy 7y ago
> I don't think there is an ML which could extract the take aways from each section There's plenty of abstractive and extractive methods for text summarization. In fact, that's what I'm working on right now, is to develop a news article summarization system for longer articles. If you're interested in learning more, there's an excellent survey paper on ArXiv: https://arxiv.org/abs/1812.02303 https://arxiv.org/abs/1812.02303
- nudpiedo 7y ago
  is there any working demo to play with? I am not into ML and won't read the whole paper... I expect these sort of mechanisms to make some weighting of the morphemes and sentences and replicate the top score ones whereas a human made summary is based on context and semantics given by humans (for example, the relevant part might be only relevant during the current zeitgeist or for some peoples bias)
  - applecrazy 7y ago
    I'm not aware of any working demos. In terms of the weighting, your intuition is correct, there's a way how a machine learning algorithm can learn which tokens (what you call morphemes) are of significance automatically. That's called an attention mechanism. It learns what tokens are important and then passes that into a "context vector" of sorts, which is then passed into the generator network (which is some form of recurrent network). The disadvantage of using only token-based attention is that it's still not enough context for large spans of text (> 1000 tokens), and I'm actually having this issue now in my work. That's where things like applying attention to chunks of text is helpful. I'm still trying to fully understand attention mechanisms, so I hope this comment made sense.