Text-Summarization | Python implementation of exhaustive & text rank | Natural Language Processing library

by PawanKartikS Python Version: Current License: No License

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kandi X-RAY | Text-Summarization Summary

Text-Summarization is a Python library typically used in Artificial Intelligence, Natural Language Processing, Bert applications. Text-Summarization has no bugs, it has no vulnerabilities and it has low support. However Text-Summarization build file is not available. You can download it from GitHub.

A basic from the scratch C++ & Python implementation of exhaustive & text rank methods of text summarization.

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Text-Summarization has a low active ecosystem.

It has 4 star(s) with 1 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

Text-Summarization has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Text-Summarization is current.

Quality

Text-Summarization has no bugs reported.

Security

Text-Summarization has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

Text-Summarization does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

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Text-Summarization releases are not available. You will need to build from source code and install.

Text-Summarization has no build file. You will be need to create the build yourself to build the component from source.

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Text-Summarization Key Features

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Text-Summarization Examples and Code Snippets

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Community Discussions

Trending Discussions on Text-Summarization

Why do you need a threshold when tokenizing a text corpus?

R: Error in textrank_sentences(data = article_sentences, terminology = article_words) : nrow(data) > 1 is not TRUE

Using Transformer for Text-Summarization

Text summary in R for multiple rows

Error Expected object of device type cuda but got device type cpu for argument #1 'self' in call to _th_index_select

QUESTION

Why do you need a threshold when tokenizing a text corpus?

Asked 2021-Jun-26 at 18:48

So I'm a self-learning NLP and came across this kaggle notebook that does text summarization using an LSTM. When it makes an orderedDict of words to integers, there's some code that apparently calculates the percentage of rare words in the vocabulary:

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ANSWER

Answered 2021-Jun-26 at 18:48

The threshold gives you a chance to ignore "rare" words that wouldn't contribute that much to bag-of-words processing. Similarly, you might want to have an upper threshold so that you could ignore words like "the", "a", etc. that, because of their pervasiveness, also don't contribute much to distinguishing among sentence classes.

Source https://stackoverflow.com/questions/68145256

QUESTION

R: Error in textrank_sentences(data = article_sentences, terminology = article_words) : nrow(data) > 1 is not TRUE

Asked 2021-Apr-07 at 05:11

I am using the R programming language. I am trying to learn how to summarize text articles by using the following website: https://www.hvitfeldt.me/blog/tidy-text-summarization-using-textrank/

As per the instructions, I copied the code from the website (I used some random PDF I found online):

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ANSWER

Answered 2021-Apr-07 at 05:11

The link that you shared reads the data from a webpage. div[class="padded"] is specific to the webpage that they were reading. It will not work for any other webpage nor the pdf from which you are trying to read the data. You can use pdftools package to read data from pdf.

Source https://stackoverflow.com/questions/66979242

QUESTION

Using Transformer for Text-Summarization

Asked 2020-Oct-25 at 21:39

I am using huggingface transformer models for text-summarization. Currently I am testing different models such as T5 and Pegasus. Now these models were trained for summarizing Big Texts into very short like a maximum of two sentences. Now I have the task, that I want summarizations, that are about half the size of the text, ergo the generated summaries are too small for my purpose.

My question now is, if there is a way to tell the model that another sentence came before? Kind of similar to the logic inside stateful RNNs (although I know they work completly different). If yes, I could summarize small windows over the sentences always with the information which content came before.

Is that just a thing of my mind? I cant believe that I am the only one, who wants to create shorter summaries, but not only 1 or two sentence long ones.

Thank you

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ANSWER

Answered 2020-Oct-25 at 21:39

Why not transfer learning? Train them on your specific texts and summaries.

I trained T5 on specific limited text over 5 epoch and got very good results. I adopted the code from here to my needs https://github.com/patil-suraj/exploring-T5/blob/master/t5_fine_tuning.ipynb

Let me know if you have a specific training questions.

Source https://stackoverflow.com/questions/63904821

QUESTION

Text summary in R for multiple rows

Asked 2020-Oct-03 at 16:02

I have a set of short text files that I was able to combine into one datatest so that each file is in a row.

I am trying to summarize the content using the LSAfun package using the generic function argument genericSummary(text,k,split=c(".","!","?"),min=5,breakdown=FALSE,...)

This works very well for single text entry, however it does not in my case. In the package explanation it says that the text input should be "A character vector of length(text) = 1 specifiying the text to be summarized".

Please see this example

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ANSWER

Answered 2020-Oct-03 at 16:02

Check class(dd$text). It's a factor, which is not a character.

The following works:

Source https://stackoverflow.com/questions/64185994

QUESTION

Error Expected object of device type cuda but got device type cpu for argument #1 'self' in call to _th_index_select

Asked 2020-Aug-02 at 01:50

I have the following code taken directly from here with some pretty little modifications:

...

ANSWER

Answered 2020-Aug-02 at 01:50

Try explicitly moving your model to the GPU.

Source https://stackoverflow.com/questions/63211463

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install Text-Summarization

You can download it from GitHub.
You can use Text-Summarization like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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