Currently, there's an abundant volume of articles available on the internet and that's where a text summarization comes in handy.
It can compress a given article by keeping only important and relevant data that provides the gist of the orginal document.
The implementation includes abstractive (Long Short Term Memory NN, Deep Recurrent NN, and Seq2Seq NN) and extractive (Text Ranking, Sentence Ranking, Graph Based Approach) techniques.
The output of a summary is judged by a custom metric that is a combination of ROUGE-N and GLEU.
I further tweak the summarization engine to tailor the needs of teachers, students, foreign language students and authors.