  Abstractive summary generation is a challenging task that requires the model
to comprehend the source text and generate a concise and coherent summary that
captures the essential information. In this paper, we explore the use of an
encoder/decoder approach for abstractive summary generation in the Urdu
language. We employ a transformer-based model that utilizes self-attention
mechanisms to encode the input text and generate a summary. Our experiments
show that our model can produce summaries that are grammatically correct and
semantically meaningful. We evaluate our model on a publicly available dataset
and achieve state-of-the-art results in terms of Rouge scores. We also conduct
a qualitative analysis of our model's output to assess its effectiveness and
limitations. Our findings suggest that the encoder/decoder approach is a
promising method for abstractive summary generation in Urdu and can be extended
to other languages with suitable modifications.
