# Summarise text

- [Gentle introduction to Text summarisation](https://www.linkedin.com/posts/ajitjaokar_a-gentle-introduction-to-text-summarization-activity-6620354936465092608-M2bI)
- [Understand Text Summarization and create your own summarizer in python](https://towardsdatascience.com/understand-text-summarization-and-create-your-own-summarizer-in-python-b26a9f09fc70)
- [An Introduction to Text Summarization using the TextRank Algorithm (with Python implementation)](https://www.analyticsvidhya.com/blog/2018/11/introduction-text-summarization-textrank-python/)
- [Beyond bag of words: Using PyTextRank to find Phrases and Summarize text](https://medium.com/@aneesha/beyond-bag-of-words-using-pytextrank-to-find-phrases-and-summarize-text-f736fa3773c5)
- [𝗘𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝘃𝗲 𝗦𝘂𝗺𝗺𝗮𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗮𝘀 𝗧𝗲𝘅𝘁 𝗠𝗮𝘁𝗰𝗵𝗶𝗻𝗴, check out the GitHub and Paper](https://www.linkedin.com/posts/philipvollet_nlp-nlproc-deeplearning-activity-6658954592727908352-417j)
- [Build a simple text summarisation tool using NLTK](https://medium.com/@wilamelima/build-a-simple-text-summarisation-tool-using-nltk-ff0984fedb4f)
- Summarise Text with TFIDF in Python: [Post 1/2](https://towardsdatascience.com/tfidf-for-piece-of-text-in-python-43feccaa74f8) | [Post 2/2](https://medium.com/@shivangisareen/summarise-text-with-tfidf-in-python-bc7ca10d3284)
- [How to Make a Text Summarizer - Intro to Deep Learning #10 by Siraj Raval](https://www.youtube.com/watch?v=ogrJaOIuBx4)
- [Summarization Task using Bart and T5 models from Hugging Face 😷 Transformers check out the code! ](https://www.linkedin.com/posts/philipvollet_nlp-deeplearning-machinelearning-activity-6650990213931769856-Hp1m)
- [A Transformer-based Approach for Source Code Summarization](https://www.linkedin.com/posts/philipvollet_nlp-machinelearning-deeplearning-activity-6663306539186565120-KVi7)
- [PEGASUS (Google AI): Pre-training with Extracted Gap-sentences for Abstractive Summarization now availabl](https://www.linkedin.com/posts/philipvollet_opensource-artificialintelligence-deeplearning-activity-6703690764825821184-aklm)
- [Bring abstractive text summarization to the next level with PEGASUS — Pre-training with Extracted Gap-sentences for Abstractive SUmmarization Sequence-to-sequence](https://www.linkedin.com/posts/nlp-zurich_nlproc-naturallanguageprocessing-textsummarization-activity-6677537748833841152-EwHb)
- [Select, Extract and Generate: Neural Keyphrase Generation with Syntactic Guidance - Deep neural sequence-to-sequence frameworks](https://www.linkedin.com/posts/philipvollet_nlp-machinelearning-activity-6697041747702353920-Y5Yw)
- [Document-level Event-based Extraction Using Generative Template-filling Transformers for NLP Tasks -](https://www.linkedin.com/posts/philipvollet_nlp-machinelearning-datascience-activity-6703563745299443712-F_1m)
- **Better NLP Summarisers**: Notebooks: [Google Colab](https://github.com/neomatrix369/awesome-ai-ml-dl/blob/master/examples/better-nlp/notebooks/google-colab/better_nlp_summarisers.ipynb) | [Jupyter](https://github.com/neomatrix369/awesome-ai-ml-dl/blob/master/examples/better-nlp/notebooks/jupyter/better_nlp_summarisers.ipynb) | [Kaggle Kernel](https://www.kaggle.com/neomatrix369/better-nlp-summarisers-notebook) | [Code](https://github.com/neomatrix369/awesome-ai-ml-dl/tree/master/examples/better-nlp/library/org/neomatrix369)
- [Code-Mixing _ya_ Code-Switching ?](code-mixing.md)
- [Quantitative Measures for Modeling a Code-Mixed corpus!](measures.md)
- [Colab: Deep Learning-Driven Summarization & Explainability with Reuters News](https://colab.research.google.com/drive/1edmnmznR97l9g3dFtA3wFew218---LNk?usp=sharing#scrollTo=Pc9gqNQ_tjtZ)

# Contributing

Contributions are very welcome, please share back with the wider community (and get credited for it)!

Please have a look at the [CONTRIBUTING](../CONTRIBUTING.md) guidelines, also have a read about our [licensing](../LICENSE.md) policy.

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