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From https://news.ycombinator.com/item?id=34659668 :

>> How do the responses compare to auto-summarization in terms of Big E notation and usefulness?

> Automatic summarization: https://en.wikipedia.org/wiki/Automatic_summarization

> "Automatic summarization" GH topic: https://github.com/topics/automatic-summarization

Though now archived,

> Microsoft/nlp-recipes lists current NLP tasks that would be helpful for a docs bot: https://github.com/microsoft/nlp-recipes#content

NLP Tasks: Text Classification, Named Entity Recognition, Text Summarization, Entailment, Question Answering, Sentence Similarity, Embeddings, Sentiment Analysis, Model Explainability, and Auto-Annotatiom



On further review, there are more GitHub projects labeled with https://github.com/topics/text-summarization than "automatic-summarization"; e.g. awesome-text-summarization: https://github.com/icoxfog417/awesome-text-summarization and https://github.com/luopeixiang/awesome-text-summarization , which links to what look like relatively current benchmarks for SOTA performance in text summarization from the gh source repo of https://nlpprogress.com/ : https://github.com/sebastianruder/NLP-progress/blob/master/e...




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