RSS Feed Summarizer

What's it do?

Modzy includes a variety of natural language processing (NLP) models such as sentiment analysis, summarization, named entity recognition, machine translation, etc. to get you started. In this simple example, we apply three models – Named Entity Recognition, Text Topic Modeling and Text Summarization – to an RSS feed to create a summary of the articles and a characterization of the feed.

Ingredients

• An RSS Feed
• A Jupyter Notebook
• Modzy's Python SDK
• Beautiful Soup
• Modzy's Named Entity Recognition, English model
• Modzy's Text Topic Modeling model
• Modzy's Text Summarization model

Instructions

Final Dish

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['data', 'software', 'systems', 'information', 'user', 'web', 'file', 'version', 'application', 'code']

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['data', 'software', 'systems', 'information', 'user', 'web', 'file', 'version', 'application', 'code']

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['data', 'software', 'systems', 'information', 'user', 'web', 'file', 'version', 'application', 'code']

Models Used

Named entity recognition

Also known as entity extraction, this model detects and classifies named entities in an English text into four categories: persons, locations, organizations, and miscellaneous. This bidirectional, pre-trained model is based on Google's BERT (Bidirectional Encoder Representations from Transformers) architecture and uses the TensorFlow deep learning framework. It was trained on the CoNLL-2003 training dataset of news wire articles from the Reuters Corpus and has a precision of 98.15%, recall of 90.61%, and F1 score of 89.72%.

Text Topic Modeling

This model, based on the unsupervized Latent Dirichlet Allocation (LDA) algorithm trained on the entire English Wikipedia corpus, takes unstructured text as input and returns the top ten topics.

Summarization

This model, derived from the Fast Abstractive Summarization-RL, summarizes a text document model. It was trained on over 300,000 news articles from CNN and Daily Mail and their human drafted summaries, and achieves a ROUGE score of 0.33. Our implementation using a GPU and parallel decoding results in 10-20x performance increase over the previous best neural abstractive summarization system.