Abstractive Text Summarization for Tweets

Abstract

In the high-tech age, we can access a vast number of articles, information, news, and opinion online. The wealth of information allows us to learn about the topics we are interested in more easily and cheaply, but it also requires us to spend an enormous amount of time reading online. Text summarization can help us save a lot of reading time so that we can know more information in a shorter period. The primary goal of text summarization is to shorten the text while including as much vital information as possible in the original text so fewer people use this strategy on tweets since tweets are commonly shorter than articles or news. However, as social networking software becomes more widespread, Text summarization can assist us in swiftly reviewing a large number of comments and discussions. In this project, we applied fuzzy logic and a neural network to extract essential sentences, followed by an abstraction model to provide a summary. Summaries generated by our model contain more vital content and obtain a better ROUGE score than classic abstraction models since we extract the crucial information first; summaries generated by our model are more similar to human-written summaries than traditional extraction models because we are using an abstract model. In the end, we provided a web-based application to display our model more interactively.