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Text Summarizer Banner NLP text summarizer, is a Python package that summarizes texts and extracts the most important sentences from a given text. Text summarizer is commonly used in news feeding websites to summarize long articles. Summarizer shortens long texts such that the summarized text preserves all the essential points of the actual text. It uses spaCy, nltk, and NumPy to do the job. This solution is also used to summarize texts (in Extractive and abstractive techniques), extract key sentences and find their TF-IDF values. You can use this package for your own projects; we are sure you'll find it useful! Extraction-based summarization involves selecting sentences from an original document and organizing them into a cohesive summary. In contrast to extraction-based summarization, abstraction-based summaries are created by using algorithms to produce abstracts that can be used as templates. spaCy is a library for Natural Language Processing (NLP). It provides functions for tokenization, part of speech tagging, and parsing. The library also includes pre-trained models for some languages. NLTK (Natural Language Toolkit) is another popular toolkit for NLP tasks. It is used in many research papers to solve different problems related to NLP. There are several popular open-source libraries available for developers:

Kit Deployment Instructions

Follow below instructions to deploy and run the solution. 1. Download, extract and double-click the kit installer file to install the kit. 2. After successful installation of the kit, press 'Y' to run the kit. 3. To run the kit manually, press 'N' and locate the zip file 'Text_Summarizer.zip' 4. Extract the zip file and navigate to the directory 'bert-extractive-summarizer-master' 5. Open command prompt in the extracted directory 'bert-extractive-summarizer-master' and run the command 'jupyter notebook' 6. Locate and open the 'Text_Summarizer.ipynb' notebook from the Jupyter Notebook browser window. 7. Execute cells in the notebook Note: Demo source code will be downloaded to local machine. It is also available here

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.

Exploratory Data Analysis

For extensive analysis and exploration of data, and to deal with arrays, these libraries are used. They are also used for performing scientific computation and data manipulation.

Text Mining

Libraries in this group are used for analysis and processing of unstructured natural language.

Machine Learning & Natural Language Processing

The library offers state-of-the-art pre-trained models for Natural Language Processing (NLP).


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