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.

翻译：抽象式摘要生成是一项具有挑战性的任务，要求模型理解源文本，并生成简洁且连贯的摘要，以捕捉核心信息。本文探讨了采用编码器-解码器方法进行乌尔都语抽象式摘要生成的研究。我们使用基于Transformer的模型，其利用自注意力机制对输入文本进行编码并生成摘要。实验表明，我们的模型能够生成语法正确且语义有意义的摘要。我们在公开数据集上对模型进行评估，在Rouge评分方面取得了当前最优结果。同时，我们对模型输出进行了定性分析，以评估其有效性与局限性。研究结果表明，编码器-解码器方法是乌尔都语抽象式摘要生成的一种有前景的方法，且经过适当修改可推广至其他语言。