The online customer reviews information available on the internet about any product consider as an essential information resource concerning customer’s interest and their knowledge of the product. It is inscribed in the form of natural language and is unstructured data. To reduce the significant information in the form of a summary is vital to the firms that work on business intelligence. It will help in product recommendation and increase in customer understanding about the product. Therefore, there is much research for creating new methodologies to summarise the text in online customer reviews automatically. In this paper, RNN-Long short-term memory Tensor Flow model along with Recall-Vocabulary Again (RVA) and Copy mechanism has used for the task of generating summary in the form of term wise from the customer reviews and news article. The RNN, along with the RVA mechanism, has been trained through a feed-forward neural network with encoder–decoder to solve the general summarization. The method has validated for the efficiency of the Giga word and DUC dataset to minimize the problem of unknown words in a decoder and generate an accurate summary that contains more vital information.

在线客户查看Internet上有关任何产品的信息，这些信息被视为与客户兴趣及其产品知识有关的重要信息资源。它以自然语言的形式刻写，并且是非结构化数据。以摘要的形式减少重要信息对于从事商业智能工作的公司至关重要。这将有助于产品推荐并增加客户对产品的了解。因此，进行了许多研究，以创建新的方法来自动汇总在线客户评论中的文本。在本文中，RNN-Long短期记忆Tensor Flow模型以及Recall-Vocabulary Again（RVA）和Copy机制用于从客户评论和新闻文章中以术语明智的形式生成摘要。RNN，连同RVA机制一起，已经通过带有编码器-解码器的前馈神经网络进行了训练，以解决一般性总结。该方法已经验证了Giga单词和DUC数据集的效率，以最大程度地减少解码器中未知单词的问题，并生成包含更多重要信息的准确摘要。