Major media companies such as The Financial Times, the Wall Street Journal or Reuters generate huge amounts of textual news data on a daily basis. Mining frequent patterns in this mass of information is critical for knowledge workers such as financial analysts, stock traders or economists. Using existing frequent pattern mining (FPM) algorithms for the analysis of news data is difficult because of the size and lack of structuring of the free text news content. In this article, we propose a Time-Aware Content Summarization algorithm to supports FPM in financial news data. The summary allows a concise representation of large volume of data by taking into account the expert's peculiar interest. The summary also preserves the news arrival time information which is essential for FPM algorithms. We experimented the proposed approach on Reuters news data and integrated it into the Streaming TEmporAl Data (STEAD) analysis framework for interactive discovery of frequent pattern.