Reading

The material on the following sites is important and should be read either before or after studying this section.

 

The following material is optional.  However, interested readers are encouraged to peruse it.

Extractive Text Summarization using Contextual Embeddings

Satish Silveri

March 9, 2021

Read: Extractive Text Summarization using Contextual Embeddings

 

 

 

 

The following sites may be used for reference.

Sentence Transformers: Multilingual Sentence, Paragraph, and Image Embeddings using BERT & Co.

 

sklearn.feature_extraction.text.TfidfVectorizer

 

sklearn.cluster.KMeans

 

Python Code

 

This section uses the Python code Sentences_KMeans_Example.py (Jupyter Notebook Sentences_KMeans_Example.ipynb).

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Digital Humanities Tools and Techniques II Copyright © 2022 by Mark Wachowiak, Ph.D. is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License, except where otherwise noted.

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