Hybrid method for framing abstractive summaries of tweets

Abstract

Ubiquitous online communication is producing massive amounts of data on an un-precedential scale. Many have come up in analyzing the data produced from Twitter. Sentiment analysis is being carried out to catch the pulse of the people. As the data is mostly in the unstructured format. Getting the summary of what has been expressed positively and what has been expressed negatively plays a major role. Summaries take a wide strand in this plait as the relevant content can't be comprehended all at once. Working on the premise that online social media conversations might represent a new source of information to monitor the status of the policies launched by the government, this investigate stands as a first paw which may ultimately be the source for producing abstractive summaries from Twitter. This paper mainly focuses on developing a hybrid method which takes the combination of extractive summaries using statistical approaches as well as abstractive summaries using Rich Semantic Graph based approaches. The set of classified positive and the negative tweets will be passed as input to the hybrid method and it generates concise as well as readable summaries from both set of tweets. These summaries can be easily viewable on the PDA's and enhances the decision making capability of the policymaker.