Automatic Summarization of Stock Market News Articles

Stock market news articles published by leading companies are read by every trader to carry out their trading activities as they provide real time and reliable information about the organization. These news articles help in analyzing and identifying essential facts in trading. If these facts can be quickly captured from the articles could lead to look for more articles for better accuracy on their decision making. This research focuses on the single document based abstractive summarization of stock market investment news articles for traders. A summarization tool to extract the salient sentences from stock market investment news article on trading is developed in this research. In methodology, A keyword based weighting in extracting the sentences are used to enrich the domain relevancy. Domain is one of the deterministic factors in summarization which helps to correctly interpret the words. Finally an efficient graph algorithm is used to obtain the fluent summary. Then these summaries were compared with the domain expert summary to identify how far the summarization is useful for the traders.