Thank you man. You explained it really well. Even a begginer like me who is mostly unfamiliar with nlp terms I can still understand everything very well.
I'm convinced that one day you can be the next Top Educator on any online platform. Great explanations, any beginner would totally understand the underlying concepts and fundamentals. I hope you reach greater heights, in years to come. Looking forward to your future videos. :)
@@NormalizedNerd Sure mate. Good to see such wonderful talents in our college. If u need any help from my side feel free to reach out, although my expertise may not be as vast as yours . XD
Hey. Thank you for your nice video. I have a question. I have course book with several chapters in English. Each chapter has a summary. But few chapters does not have any. I want to create 20 short cast videos for this course book. My goal is to create title for each video from these summaries. I would like to know how to automate the process using nlp and python? The point is that keywords or titles should be unique for each video in each chapter and should not be the same either with other video titles in other chapters. Will b great to help me.
Sir you said in the Text Ranking algorithm the scores are calculated based on the importance of the sentences. But how does the model recognize the importance of a sentence, am I missing something here?
A small doubt in the video, for keyword extraction except for denoting nodes as words the other process is same right like getting sentences from the cleaned text -> tokenizing the sentences -> sentence vector -> similarity matrix ??
In the original TextRank paper, they explain things using words as the lexical unit, but you can apply the same process using sentences. What this video didn’t explain as clearly is that if you want to get keywords, you need to repeat the same process so each node in your graph will represent a word.
Sir i am making a multiple text summarizer web based application for my final year project please help me to resolve this issue if you can 🙏 My query is i have to compare two pdfs or paragraph if both are same then how to display a popup like both are same and if different then both are different and then their summary
You need to use words instead of sentences and repeat the same process. PageRank doesn’t really produce a ranked list of the nodes, it produces a “score” value for each node (and they are typically normalized so they add up to one, i.e., a probability distribution). Of course, once you sort the scores, you get a ranked list of the nodes.
Thank you man. You explained it really well. Even a begginer like me who is mostly unfamiliar with nlp terms I can still understand everything very well.
Glad it helped!
A very clear explanation!!!!!!! Subscribed forever!!!
Such wonderful work thank you! I needed to learn about nlu for a new project and this video saved me DAYS of introductory research. Love ya!
You're very welcome!
Simply Superbbb!!! Anddd the way you explain the topic is AWESOME:)
Thank you so much 😀
I'm convinced that one day you can be the next Top Educator on any online platform. Great explanations, any beginner would totally understand the underlying concepts and fundamentals. I hope you reach greater heights, in years to come. Looking forward to your future videos. :)
Thanks a lot dada! It really means a lot. More interesting videos are coming :D
@@NormalizedNerd Sure mate. Good to see such wonderful talents in our college. If u need any help from my side feel free to reach out, although my expertise may not be as vast as yours . XD
Definitely, I'll contact you :)
I was planning to make a text summarisation model and it cleared my concept clearly. Really amazing video.
Great to hear!
Thank you so much! I was searching exactly for this.
Glad I could help!
Hey. Thank you for your nice video. I have a question. I have course book with several chapters in English. Each chapter has a summary. But few chapters does not have any. I want to create 20 short cast videos for this course book. My goal is to create title for each video from these summaries. I would like to know how to automate the process using nlp and python? The point is that keywords or titles should be unique for each video in each chapter and should not be the same either with other video titles in other chapters. Will b great to help me.
Nice tutorial "Mr.Nerd"!!!
please implement the complete page ranking algorithm for text data. as it is very usefull for recommendation system. thanks in advance.
Thanks for suggesting...I'll try to cover this in future!
Sir you said in the Text Ranking algorithm the scores are calculated based on the importance of the sentences. But how does the model recognize the importance of a sentence, am I missing something here?
Keep up the good work 😁
Thank you
Thanks!!
Amazing explanation!!! Keep it up...
Thanks!
I love your work. Great work. thanks
Thanks a lot! :D
Amazing explanation 👍
Thank you 🙂
To save time start from 12:00
can this be done on excel file with each cell in it of a column
Great video. Keep it up!
Thanks!
Can you please give me the link to the Generative Summarization Technique?
Thank you so much sir
A small doubt in the video, for keyword extraction except for denoting nodes as words the other process is same right like getting sentences from the cleaned text -> tokenizing the sentences -> sentence vector -> similarity matrix ??
word vector instead of sentence vector
@@NormalizedNerd thank you so much
Hi
iam following ur steps but in my its showing text is not define can u let me know How I define txt
Gensim.summarization is removed in 4.x versions. Could you provide an alternative?
Sir, i want to scoring the extraction keyword based on reference answer. so if more keyword student use he will the high score. Might you know? thanks
is this skill applied only on unstructured(paragraphs) or we can use it on customers' feedbacks(rows)...?
This can be applied to any text data.
No module named 'gensim.summarization'
Sir how to create abstractive summary??
Need one video
Are there future voice recognition lessons Thank you for your valuable lessons.
It's in my bucket list! Keep supporting...
You calculated a similarity matrix for the sentences and then "magically" derived word similarities to create your graph. Please explain
In the original TextRank paper, they explain things using words as the lexical unit, but you can apply the same process using sentences. What this video didn’t explain as clearly is that if you want to get keywords, you need to repeat the same process so each node in your graph will represent a word.
Really interested in generative summarization, are there any resources you can recommend that I can use to learn this?
@flawed perfection I guess you'll find these papers helpful:
paperswithcode.com/task/abstractive-text-summarization?page=2
@@NormalizedNerd Thank you 👌
sir, I want to implement the abstractive text summarization based on deep learning .please provide an explanation with a coded link for that.
Thanks for the suggestion.
Please make video about question answering system keras
Thanks for your suggestion! I'll try to make one.
Sir i am making a multiple text summarizer web based application for my final year project please help me to resolve this issue if you can 🙏
My query is i have to compare two pdfs or paragraph if both are same then how to display a popup like both are same and if different then both are different and then their summary
I want to find summary for individual pages of pdf.. like there are word vectors, sentence vectors...I want to find page vectors. How can I do that?
you should use doc2vec models.
sir i want to implement the text summarization based on graph based algorithm ....sir please provide me the code links for help
Check this: towardsdatascience.com/understand-text-summarization-and-create-your-own-summarizer-in-python-b26a9f09fc70
Sir, If I need to get probabilistic values along with the extracted keywords, what I will do then?
You need to use words instead of sentences and repeat the same process. PageRank doesn’t really produce a ranked list of the nodes, it produces a “score” value for each node (and they are typically normalized so they add up to one, i.e., a probability distribution). Of course, once you sort the scores, you get a ranked list of the nodes.
Thank you.
how to extract key words from a text document?
there are several popular models for keyword extraction like keyBERT.
doesnt work anymore. gensim.summarization was removed with gensim 4.0
Any link to the python code ?
Thanks for the tutorial on text summarization using Gensim
Here it is github.com/Suji04/NormalizedNerd/blob/master/Introduction%20to%20NLP/Summarization%20%26%20Keyword%20Extraction.ipynb
I love your channel. Best possible explanations!!! May I get your contact sir???
I'm glad you liked it. You can dm me on twitter twitter.com/nomalized_nerd
What is your twitter account
@normalized_nerd