There is plethora of information present on the web, on a given topic, in different forms i.e. blogs, articles, websites, etc. However, not all of the information is useful. Perusing and going through all of the information to get the understanding of the topic is a very tiresome and time-consuming task. Most of the time we end up investing in reading content that we later understand was not of importance to us. Due to the lack of capacity of the human to grasp vast quantities of information, relevant and crisp summaries are always desirable. Therefore, in this paper, we focus on generating a new blog entry containing the summary of multiple blogs on the same topic. Different approaches of clustering, modelling, content generation and summarization are applied to reach the intended goal. This system also eliminates the repetitive content giving savings on time and quantity, thereby making learning more comfortable and effective. Overall, a significant reduction in the number of words in the new blog generated by the system is observed by using the proposed novel methodology.

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相关博客对自然语言处理的概述

在网络上，关于给定主题的信息过多，以博客，文章，网站等不同形式出现。但是，并非所有信息都是有用的。仔细阅读并遍历所有信息以获得对主题的理解是一项非常繁琐且耗时的任务。在大多数情况下，我们最终会投资阅读我们后来认为并不重要的内容。由于人类缺乏掌握大量信息的能力，因此始终需要相关且清晰的摘要。因此，在本文中，我们着重于生成一个新的博客条目，其中包含有关同一主题的多个博客的摘要。可以采用不同的聚类，建模，内容生成和汇总方法来达到预期的目标。该系统还消除了重复的内容，从而节省了时间和数量，从而使学习更加舒适和有效。总体而言，通过使用建议的新颖方法，可以观察到系统所生成的新博客中单词的数量显着减少。