#pagerank #textrank #nlp
This video discusses one of the baselines paper in Extractive Summarization and Keyword Extraction domain in Natural Language Processing. The idea that authors propose is to represent given text unit as node in the graph followed by applying some ranking method to score the nodes based on their importance in the entire graph.
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⏩ Abstract: In this paper, we introduce TextRank – a graph-based ranking model for text processing, and show how this model can be successfully used in natural language applications. In particular, we propose two innovative unsupervised methods for keyword and sentence extraction, and show that the results obtained compare favorably with previously published results on established benchmarks.
⏩ OUTLINE:
0:35 - Google PageRank in brief
7:28 - Representating Text as Graph
9:20 - TextRank for Keyword Extraction
12:31 - TextRank for Sentence Extraction
⏩ Title: TextRank: Bringing Order into Texts
⏩ Link: https://web.eecs.umich.edu/~mihalcea/papers/mihalcea.emnlp04.pdf
⏩ Author: Rada Mihalcea and Paul Tarau
⏩ Organisation: University of North Texas
⏩ IMPORTANT LINKS
Unsupervised Keyword Extraction from Raw Text - https://www.youtube.com/watch?v=ykClwtoLER8
Unsupervised Multi-Document Summarisation- https://www.youtube.com/watch?v=1jwUOMQVCo4
Long Document Summarisation -- https://www.youtube.com/watch?v=2IzXW3Ypks0
Text Summarisation on COVID-19 research articles - https://www.youtube.com/watch?v=kC5kP1dPAzc
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About Me:
I am Prakhar Mishra and this channel is my passion project. I am currently pursuing my MS (by research) in Data Science. I have an industry work-ex of 3 years in the field of Data Science and Machine Learning with a particular focus in Natural Langauge Processing (NLP).
#techviz #datascienceguy #emnlp #graph #ranking
