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Misc. Talks

The following video provides more detail about the graph-based approaches used in TextRank, as well as trade-offs, benchmarks, comparisons with other algorithms, and so on:

"Single and Multiple Document Summarization with Graph-based Ranking Algorithms"
Rada Mihalcea
Microsoft Research on YouTube (2016-09-05)

For a general overview of this Python library and its usage, see:

"Graph-Based Data Science"
Paco Nathan
Derwen (2021-01-30)

Paco Nathan
IIT Patna (2020-02-21)

For related course materials and training, please check for calendar updates in the article "Natural Language Processing in Python".


In general, see listings on Google Scholar for citations of this work.

"Knowledge Graph-based Core Concept Identification in Learning Resources"
Rubén Manrique, Christian Grevisse, Olga Mariño, Steffen Rothkugel
8th Joint International Conference, JIST 2018 (2018-11-26)

"Use TextRank to Extract Most Important Sentences in Article"
Ceshine Lee
The Artificial Impostor (2018-12-09)

"Automatic Keyword extraction using TextRank in Python"
Anindya Naskar
ThinkInfi (2018-09-30)

Practical Data Analysis: Using Python & Open Source Technology
Dhiraj Bhuyan (2018-07)

"Beyond bag of words: Using PyTextRank to find Phrases and Summarize text"
Aneesha Bakharia
Medium (2017-10-11)

"Text Summarization in Python: Extractive vs. Abstractive techniques revisited"
Pranay Mathur, Aman Gill, Aayush Yadav
RaRe Technologies (2017-04-05)


The spaCy pipeline version of PyTextRank was originally written very late one evening near the beach in A Coruña, Galica, while the author was teaching an NLP seminar at the university during Big Data Coruña 2019.

What a beautiful city, there within ancient Celtic lands, guarded by the Tower of Hercules. It's more than a bit reminiscent of Santa Cruz, California – or perhaps the other way around.

Many thanks to Professor Amparo Alonso Betanzos, Catedrática de Ciencias de la Computación e Inteligencia Artificial de la UDC, and to the Universidade da Coruña.

Last update: 2021-04-03