Different Text Summarization Techniques Using Langchain

Text summarization is an NLP task that creates a concise and informative summary of a longer text. LLMs can be used to create summaries of news articles, research papers, technical documents, and other types of text.
Summarizing large documents can be challenging. To create summaries, you need to apply summarization strategies to your indexed documents. You have already seen some of these strategies in the previous notebooks. If you haven't completed it, it is recommended to do so to have a basic understanding of how to summarize large documents.
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[@krishnaik06]

Just trying new things...I hope you like it :)

[@RamaChandran-fc3hp]

Yes sir i liked it

[@janey9932]

You are amazing, thank you for this it was more engaging, thank you very much

[@rohitkumbhar4689]

Much needed

[@priyabratashee4975]

Very nice new one but your old setup will forever gold ❤ loved that

[@bhavinparmar4991]

This video is as usual very informative. Regarding the setup - I vote for the old setup. In this new setup, around 30%-40% screen is getting covered by your movement and we are not able to see the screen entirely which was not the case in earlier setup. If you want to adept to this new setup then keep some space between the code screen and yourself.

[@drjodyannjones]

love the new video format Krish!

[@vagheeshmk3156]

Krish and his tutorials rocking as always. And Krish's makeover is an added bonus

[@palashh9]

Loved this new setup feel more grasping

[@salimsikder2364]

I dont know how to thank you Sir! You save me every single time!!!

[@k.k.gayansanjeewa7432]

you pretty much go though the langchain summarize document page . but have to admit , this is faster and on to the point . So Thank you!

[@sujithreddym5166]

You were wrong about Refine method It doesn't use chunk 1, 12, 123, 1234.n It takes the summary of the first chunk and combines with the next chunk saying "till now this is the summary, can we refine it by this additional text"

[@arslanabid2245]

omg! love the setup

[@adriandominiquearante3197]

I've read many comments from the internet that langchain is not a production grade framework. Meaning it is not advisable to use langchain in the production. Is it true Krish??

[@abhijitsaptal2492]

informative...nice explaination

[@faheemkhan9786]

Very Good Setup . I am waiting for real business dataset Project

[@manjitbaishya3996]

Love this setup sir! Amazing🔥🔥🔥🔥🔥🔥🔥🔥

[@haoshi4189]

Dear Krish, thank you for this video. for the last part summary chunks, how can we indicated the length of summary & the selected language for the final output ?

[@CodeWonders_]

Do more video like this

[@The845548]

Great video. Thankyou for sharing. Btw, I liked yourearlier setup more.

[@ShahZ]

Great stuff Krish, love the new setup. Btw, can you do a text summarization where the conversation is taking place between a customer and support agent.

[@vitalikbutarin6580]

I think the old view was good because here things are getting covered behind your movement

[@darshanrokkad5216]

same

[@CodeWonders_]

No

[@SumanAmara]

nice enviroment like stack market back ground screen graph 🙂

[@debatradas1597]

thank you so much

[@zahirajahannawabjan6185]

Very useful and nice sir

[@izainonline]

Great video just a suggestion sir whatever you mention to go to your referred video,plz include that link in your detail part. Thanks

[@chetaningavle6167]

❤❤❤

[@chinnibngrm272]

Waiting for End To End project with good UI sir....

[@vishnujangid7811]

Hi Krish Sir, can we do some question answer based on a repository as well, like i gave the full github repo and asking the question like what is the data flow, how many flow present in this repository. what is the agenda of the repo, etc... is yes how we can do it any guidance ?

[@ethanfrost2708]

thank you!

[@user-tb9co5ll2i]

Sir please teach azure with ML.

[@neverfailify]

Is it possible to control the lengh of the summary? like summarize in about 200 words or summarize in 50 words

[@ItsCloudHub]

Can we do analysis of incident dump and find out opportunities for improvements

[@pssab8]

Is it possible to get deterministic summary from these techniques??

[@kartik_exe_]

Sir its a very informative video but do make sure to hide your api key aur maybe after the video delete that ai api key

[@yasminesmida2585]

can i use the map reduce technique to generate french summary?

[@abinayakamaraj2697]

Sir please teach block chain

[@heetha4281]

Hi Krish, I have doubts about Refine chain type if we are combining chunks for every chunk for the last chunk it will get the whole documentation again right then why does the need to divide the document into chunks

[@leolau5962]

Refine is using the summary from chunk 1 and combine with chunk 2 and push to LLM.

[@Arkantosi]

So assume I have 30 000 customer reviews for one product. How can I ask the LLM to summarize all the reviews into one master review?

[@arbindchoubey9698]

how did you validate the result? What metric did you use?

[@Coff03]

How to increase the length of summary in StuffDocumentChain Text summarization

[@rishiraj2548]

Namaste

[@sathyag2608]

Hi can you share gen ai playlist

[@user-oi6rs3fp2b]

except ImportError: --> 302 raise ImportError( 303 "Could not import openai python package. " 304 "Please install it with `pip install openai`." getting this error sir

[@Coff03]

!pip install openai (and then in next cell) import openai

[@ankurkaiser]

better create a requirements.txt file in the same directory and put all the required packages and run it later pip install -r requirements.txt. It will solve the issue

[@user-vk7mw4po4n]

The token size is 8192 precisely

[@neil007sal]

Modi ka naam sunke Mood off ho gaya..puri duniya me ye Desh dhrohi ka speech hi mila ?

[@avixek]

I liked your old setup earlier where there was the visibility of the code and we can get the walk through.. this is just talking so I prefer the previous setup even if it was just copy paste from any different notebook. The previous setup would allow us to see where the code could go wrong. This seems more like a lecture format which everyone is doing. TLDR; liked your old setup. Also you look uncomfortable!!

[@chetaningavle6167]

😂