A Comprehensive Technique for User Activity Based Twitter Content Summarization

Abstract

Going through thousands of comments in order to understand opinion of people on a particular post ingests in a lot of time and resources of the user. By developing this system, we aim that user gets updated with summarized information of all such events in a time constrained manner. It involves merging multiple opinions stated on the social platform and summarizing it to provide the gist of the topic in order to improve ergonomic experience. For this purpose, our system displays both abstractive and extractive summary of the content. Extractive summary generation makes use of Page rank algorithm and abstractive summary generation makes use of RNN (LSTM).