Supporting stock trading in multiple foreign markets: a multilingual news summarization approach

In today's world, plenty of textual news on stock markets written in different languages are available for traders, financial promoters, and private investors. However, their potential in supporting trading in multiple foreign markets is limited by the large volume of the textual corpora, which is practically unmanageable for manual inspection. Although, text mining and information retrieval techniques allow the automatic generation of interesting summaries from document collections, the study and application of multilingual summarization algorithms to financial news is still an open research problem. This paper addresses the summarization of collections of financial documents written in different languages to enhance the financial actor's awareness of foreign markets. Specifically, the proposed mining system (i) is able to cope with news written in multiple languages, (ii) generates multiple-level summaries covering specific and high-level concepts in separate sections, on behalf of users with different skill levels, and (iii) ranks the summary con- tent based on both objective and subjective quality indices. These features are taking an increasingly important role in financial data summarization. As a case study, a preliminary implementation of the proposed system has been presented and validated on real multilingual news ranging over stocks of different markets. The preliminary results show the effectiveness and usability of the proposed approach.