text_summurization_abstractive_methods

Why we included this project

Abstractive text summarization is a dense subject, and this repo makes it easier to approach by packing three different model designs into self-contained Colab notebooks. You get a sequence-to-sequence baseline with a bidirectional LSTM encoder and attention, a pointer-generator variant that cuts down repetitive output, and a reinforcement-learning version, all ready to run with data loaded from Google Drive. That matters if you want to experiment without owning a powerful GPU, since everything runs in Google's free environment. The examples also cover Hindi, Amharic, and Arabic in addition to English, which is handy for prototyping in less common languages. Each notebook is backed by tutorial posts and a published paper, so you can follow along while learning the underlying ideas.