  Text Summarization is the task of condensing long text into just a handful of
sentences. Many approaches have been proposed for this task, some of the very
first were building statistical models (Extractive Methods) capable of
selecting important words and copying them to the output, however these models
lacked the ability to paraphrase sentences, as they simply select important
words without actually understanding their contexts nor understanding their
meaning, here comes the use of Deep Learning based architectures (Abstractive
Methods), which effectively tries to understand the meaning of sentences to
build meaningful summaries. In this work we discuss one of these new novel
approaches which combines curriculum learning with Deep Learning, this model is
called Scheduled Sampling. We apply this work to one of the most widely spoken
African languages which is the Amharic Language, as we try to enrich the
African NLP community with top-notch Deep Learning architectures.
