Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Natural Language Processing and Computational Linguistics

You're reading from   Natural Language Processing and Computational Linguistics A practical guide to text analysis with Python, Gensim, spaCy, and Keras

Arrow left icon
Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788838535
Length 306 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Bhargav Srinivasa-Desikan Bhargav Srinivasa-Desikan
Author Profile Icon Bhargav Srinivasa-Desikan
Bhargav Srinivasa-Desikan
Arrow right icon
View More author details
Toc

Table of Contents (17) Chapters Close

Preface 1. What is Text Analysis? FREE CHAPTER 2. Python Tips for Text Analysis 3. spaCy's Language Models 4. Gensim – Vectorizing Text and Transformations and n-grams 5. POS-Tagging and Its Applications 6. NER-Tagging and Its Applications 7. Dependency Parsing 8. Topic Models 9. Advanced Topic Modeling 10. Clustering and Classifying Text 11. Similarity Queries and Summarization 12. Word2Vec, Doc2Vec, and Gensim 13. Deep Learning for Text 14. Keras and spaCy for Deep Learning 15. Sentiment Analysis and ChatBots 16. Other Books You May Enjoy

Summarizing text

Often in text analysis, it is useful to summarize large bodies of text either to have a brief overlook of the text before deeply analyzing it or identifying the keywords in a text. It is also often the end game a text analysis task of its own. We will not be working on building our own text summarization pipeline, but rather focus on using the built-in summarization API which Gensim offers us.

It is important to remember that the algorithms included in Gensim do not create its own sentences, but rather extracts the key sentences from the text which we run the algorithm on. This summarizer is based on the TextRank algorithm, from an article by Mihalcea and others, called TextRank [10]. This algorithm was later improved upon by Barrios and others in another article, Variations of the Similarity Function of TextRank for Automated Summarization ...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image