GenSumm: A Joint Framework for Multi-Task Tweet Classification and Summarization Using Sentiment Analysis and Generative Modelling

Diksha Bansal, Rahul Grover, Naveen Saini, Sriparna Saha

Abstract: Social media platforms like Twitter act as the medium for communication among people, government agencies, NGOs, and other relief providing agencies in widespread humanitarian havoc during a disaster outbreak when other communication means might not be available. Various agencies leverage Twitter's open and public features to get timely and reliable updates, thus support agencies in communicating with the people on rescue and provide immediate relief. As situational updates are mixed in millions of other tweets, an efficient system is required to extract and summarize these tweets. We have developed a novel framework that uses a deep learning-based classification model to separate the informational tweets from others and summarizes them in the current paper. Non-situational tweets mostly comprise sentiments like grief, anger, sorrow, etc. Motivated by this observation, we have solved sentiment classification and informative tweet selection tasks simultaneously using a multi-task learning (MTL) in a deep-learning framework. Our summarization approach generates clustering solutions using various existing approaches and then ensembles cluster solutions using generative modelling. A summary is formulated by extracting tweets from different clusters. The proposed approach's superior performance on four disaster-related events indicates the developed framework's efficiency over the state-of-the-art techniques.

External IDs:dblp:journals/taffco/BansalGSS24