Text Summarization Using spaCy
Text summarization is the process of distilling the most important information from a source (or sources) to produce an abridged version for a particular user (or users) and task (or tasks).I have one article on this.
🎬 Introdruction Wine Reviews
In this repository, I will try to explore the Wine Reviews Dataset. It contains 130k of reviews in Wine Reviews. And at the end of this repository, I will try to make simple text summarizer that will summarize given reviews. The summarized reviews can be used as a reviews title also.I will use Spacy as natural language processing library for handling this project.
📋 Object Of This Project
The objective of this project is to build a model that can create relevant summaries for reviews written on Wine reviews. This dataset contains above 130k reviews, and is hosted on Kaggle.
Prerequisites
This repository makes the following assumptions:
- You are familar with Python
- You have Python 3.6 or greater installed on your system
- spaCy package.
What is spaCy?
spaCy is a relatively new package for "Industrial strength NLP in Python" developed by Matt Honnibal at explosion.ai. It is designed with the applied data scientist in mind, meaning it does not weigh the user down with decisions over what esoteric algorithms to use for common tasks and it's fast. Incredibly fast (it's implemented in Cython). If you are familiar with the Python data science stack, spaCy is your numpy for NLP – it's reasonably low-level, but very intuitive and performant.However, since SpaCy is a relative new NLP library, and it's not as widely adopted as NLTK.
Installation of spaCy
spaCy, its data, and its models can be easily installed using python package index and setup tools. Use the following command to install spacy in your machine:
! pip install spacy
In case of Python3, replace "pip" with "pip3" in the above command.
OR download the source from here and run the following command, after unzipping:
!python setup.py install
To download all the data and models, run the following command, after the installation:
!python -m spacy.en.download all
You are now all set to explore and use spacy.
Implementation Section
Implementation section complete by Kaggle Kernel and Jupyter notebook.Check the kernel or Jupyter notebook.
Conclusion
If you have any suggestion feel free to reach me by sent email or connect on LinkedIn. Stay in touch for more update. Thank you.
"Let us celebrate the occasion with wine and sweet words."
If you like this repository then fork this repository. Happy Analysis!
