Automatic Document Summarizer part 2

To use Python IDE Pycharm or PyDev to do document summarization of 10 sets of self-extracted documents from the web.

The program structures are as follow:

a) Acquiring the the 10 sets of documents > Data cleaning > Representation Learning (Bag-of-Words & Tf-idf) > Reduce Matrix (Latent Semantic Analysis) > Clustering (K-means) (completed)

b) Acquiring the the 10 sets of documents > Data cleaning > Representation Learning (Word Vector & Tf-idf) > Reduce Matrix (Latent Semantic Analysis) > Clustering (Sequential K-means)

c) Acquiring the the 10 sets of documents > Data cleaning > Representation Learning (Bag-of-Words & Tf-idf) > Reduce Matrix (Latent Semantic Analysis) > Clustering (Sequential K-means)

d) Acquiring the the 10 sets of documents > Data cleaning > Representation Learning (Word Vector & Tf-idf) > Reduce Matrix (Latent Semantic Analysis) > Clustering (K-means)

And finally a comparison of accuracy of the 4 different combination of techniques used

See attached picture for illustration.

Machine Learning (ML) Python

Projekt ID: #16186781

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