As human society transitions into the information age, reduction in our attention span is a contingency, and people who spend time reading lengthy news articles are decreasing rapidly and the need for succinct information is higher than ever before. Therefore, it is essential to provide a quick overview of important news by concisely summarizing the top news article and the most intuitive headline. When humans try to make summaries, they extract the essential information from the source and add useful phrases and grammatical annotations from the original extract. Humans have a unique ability to create abstractions. However, automatic summarization is a complicated problem to solve. The use of sequence-to-sequence (seq2seq) models for neural abstractive text summarization has been ascending as far as prevalence. Numerous innovative strategies have been proposed to develop the current seq2seq models further, permitting them to handle different issues like saliency, familiarity, and human lucidness and create excellent synopses. In this article, we aimed toward enhancing the present architectures and models for abstractive text summarization. The modifications have been aimed at fine-tuning hyper-parameters, attempting specific encoder-decoder combinations. We examined many experiments on an extensively used CNN/DailyMail dataset to check the effectiveness of various models.

翻译：随着人类社会迈入信息时代，注意力持续时间缩短成为必然趋势，人们花费时间阅读长篇新闻文章的数量急剧下降，对简洁信息的需求比以往任何时候都更加迫切。因此，通过简洁地总结顶部新闻文章和最直观的标题来快速提供重要新闻概览至关重要。当人类尝试撰写摘要时，他们从源文本中提取关键信息，并添加原始摘要中的有用短语和语法注释。人类拥有创造抽象表述的独特能力。然而，自动摘要是一个复杂的难题。基于序列到序列（seq2seq）模型的神经抽象文本摘要在普及程度上不断上升。研究人员提出了众多创新策略以进一步改进现有的seq2seq模型，使其能够处理显著性与熟悉度、人类可读性等不同问题，并生成优秀的摘要。本文旨在改进用于抽象文本摘要的现有架构和模型。修改方向集中于超参数调优，尝试特定的编码器-解码器组合。我们在广泛使用的CNN/DailyMail数据集上进行了大量实验，以检验不同模型的有效性。