Question-focused Summarization by Decomposing Articles into Facts and
  Opinions and Retrieving Entities
Krutika Sarode Shashidhar Reddy Javaji Vishal Kalakonnavar
This research focuses on utilizing natural language processing techniques to
predict stock price fluctuations, with a specific interest in early detection
of economic, political, social, and technological changes that can be leveraged
for capturing market opportunities. The proposed approach includes the
identification of salient facts and events from news articles, then use these
facts to form tuples with entities which can be used to get summaries of market
changes for particular entity and then finally combining all the summaries to
form a final abstract summary of the whole article. The research aims to
establish relationships between companies and entities through the analysis of
Wikipedia data and articles from the Economist. Large Language Model GPT 3.5 is
used for getting the summaries and also forming the final summary. The ultimate
goal of this research is to develop a comprehensive system that can provide
financial analysts and investors with more informed decision-making tools by
enabling early detection of market trends and events.