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How to use gensim.summarize function

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by vinitha@openweaver.com dot icon Updated: Oct 27, 2023

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Gensim is a popular open-source NLP library. It provides various tools for topic modeling, document indexing, and text summarization.


However, Gensim is primarily known for its implementation of topic modeling algorithms. It can help with text summarization.  


Here is an approach to using Gensim for text summarization of large text collections:  

  • Install Gensim  
  • Preprocess the Text document.  
  • Create a Corpus  
  • Create a Dictionary  
  • Topic Modeling  
  • Text Summarization (Extractive method, Abstractive summarization)  
  • Evaluate the Summary  
  • Tune Parameters  
  • Iterate and Optimize  


Gensim, a popular open-source library for NLP tasks. It is primarily designed for processing textual data, specifically unstructured text. It can handle various data and perform various tasks.


But it is not limited to the following:  

  • Raw Text Data  
  • Preprocessed Text Data  
  • Corpora of Documents  


Some of the popular algorithms available in Gensim include:  

  • Latent Semantic Analysis (LSA)  
  • Latent Dirichlet Allocation (LDA)  
  • Word2Vec, Doc2Vec, TextRank, PageRank.  


Gensim library stands out as a powerful tool for data analysis, particularly in the domain of NLP. Its unique aspects contribute to its popularity and utility in various applications:  

  • Diverse Functionality  
  • Efficient Implementation  
  • Ease of Use  
  • Focus on Natural Language Processing  

Fig: Preview of the output that you will get on running this code from your IDE

Code

In this solution we are using gensim library

Columnwise Summarize multiple sentences present in a list using the gensim summarizer

PythondotLines of Code : 22dotLicense : Strong Copyleft (CC BY-SA 4.0)

Dependent Libraries : 
 from gensim.summarization.summarizer import summarize


summarize("punctual in time.") 

summarize("punctual in time. good subject knowledge")     
#o/p will be blank string since the text is very small, and now you won't receive any error
''

 #example
 import pandas as pd
df = pd.DataFrame([[["good subject."," punctual in time.","discipline person."]]], columns = ['comment'])

print(df)
    comment
0   [good subject., punctual in time, discipline ...

df['comment'] = df['comment'].apply(''.join)


df['comment'].apply(summarize) #this will work for you but keep in mind you have long text to generate summary

Instructions

Follow the steps carefully to get the output easily.


  1. Download and Install the PyCharm Community Edition on your computer.
  2. Open the terminal and install the required libraries with the following commands.
  3. pip3 install gensim==3.6.0
  4. Create a new Python file on your IDE.
  5. Copy the snippet using the 'copy' button and paste it into your python file.
  6. remove the code line 3,15,16 for better understanding
  7. Run the current file to generate the output.


I hope you found this useful.


I found this code snippet by searching for ' Columnwise Summarize multiple sentences ' in Kandi. You can try any such use case!

Environment tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

  1. PyCharm Community Edition 2023.1
  2. The solution is created in Python 3.11.1 Version
  3. gensim library 3.6.0 version



Using this solution, we can able to use gensim.summarize function with simple steps. This process also facilities an easy way to use, hassle-free method to create a hands-on working version of code which would help us to use gensim.summarize function.

Dependency library

pandasby pandas-dev

Python doticonstar image 38689 doticonVersion:v2.0.2doticon
License: Permissive (BSD-3-Clause)

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

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            pandasby pandas-dev

            Python doticon star image 38689 doticonVersion:v2.0.2doticon License: Permissive (BSD-3-Clause)

            Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
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              Quality
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                  License
                    Reuse

                      gensimby RaRe-Technologies

                      Python doticonstar image 14417 doticonVersion:4.3.0doticon
                      License: Weak Copyleft (LGPL-2.1)

                      Topic Modelling for Humans

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                                gensimby RaRe-Technologies

                                Python doticon star image 14417 doticonVersion:4.3.0doticon License: Weak Copyleft (LGPL-2.1)

                                Topic Modelling for Humans
                                Support
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                                      License
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                                          You can search for any dependent library on kandi like ' gensim ' ' pandas '

                                          FAQ:  

                                          1. What is the text summarization process?  

                                          Text summarization is creating a coherent version of a document or a text. It helps in preserving its key information. There are two approaches to text summarization:   

                                          • Extractive summarization   
                                          • Abstractive summarization

                                           

                                          2. How Gensim can summarize large text collections?  

                                          Gensim is a popular Python library for topic modeling and text summarization. It can summarize large text collections using various techniques. Here's a general overview of how Gensim can help with text summarization.  

                                          • Install Gensim  
                                          • Preprocess the Text document.  
                                          • Create a Corpus  
                                          • Create a Dictionary  
                                          • Topic Modeling  
                                          • Text Summarization (Extractive method, Abstractive summarization)  
                                          • Evaluate the Summary  
                                          • Tune Parameters  
                                          • Iterate and Optimize  


                                          3. What is Latent Semantic Indexing? How does it help with natural language processing?  

                                          Latent Semantic Indexing (LSI) is also Latent Semantic Analysis (LSA). It is a technique used in NLP. It helps analyze relationships between documents and the terms they contain. 

                                          It helps with natural language processing in the following ways:  

                                          • Dimensionality Reduction  
                                          • Conceptual Understanding  
                                          • Information Retrieval and Search  


                                          4. How does summary generation work in Gensim?  

                                          Gensim provides an interface for text summarization. It is through its implementation of the TextRank algorithm. It is an unsupervised extractive summarization technique. It depends on the PageRank algorithm used by Google. 

                                          Here's an overview of how summary generation works in Gensim:  

                                          • Preprocessing  
                                          • Graph Construction  
                                          • Graph-based Ranking  
                                          • Sentence Selection  
                                          • Summary Generation  
                                          • Output  

                                            

                                          5. Why should I use Gensim for my text summarization needs?  

                                          Using Gensim for your text summarization needs can offer text summarization. Here are some reasons why you should consider using Gensim for text summarization:  

                                          • Efficient Extractive Summarization  
                                          • Ease of Implementation  
                                          • Handling Large Text Collections  
                                          • Integration with Other NLP Functionalities  
                                          • Customizability and Parameter Tuning   

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