In todays era huge volume of information exists everywhere. Therefore, it is very crucial to evaluate that information and extract useful, and often summarized, information out of it so that it may be used for relevant purposes. This extraction can be achieved through a crucial technique of artificial intelligence, namely, machine learning. Indeed automatic text summarization has emerged as an important application of machine learning in text processing. In this paper, an english text summarizer has been built with GRU-based encoder and decoder. Bahdanau attention mechanism has been added to overcome the problem of handling long sequences in the input text. A news-summary dataset has been used to train the model. The output is observed to outperform competitive models in the literature. The generated summary can be used as a newspaper headline.

翻译：在当今时代，信息大量存在于各个领域。因此，评估这些信息并从中提取有用且通常经过概括的信息，以便用于相关目的，显得至关重要。这种提取可以通过人工智能的一项关键技术——机器学习——来实现。事实上，自动文本摘要已成为机器学习在文本处理中的重要应用。本文构建了一个基于GRU编码器和解码器的英文文本摘要系统。该系统加入了Bahdanau注意力机制，以解决输入文本中处理长序列的问题。训练使用了新闻摘要数据集。结果表明，该模型的性能优于文献中的对比模型。生成的摘要可作为报纸标题使用。