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The paper presents a multi-document summarization system which builds companyspecific summaries from a collection of financial news such that the extracted sentences contain novel and relevant information about the corresponding organization. The user’s familiarity with the company’s profile is assumed. The goal of such summaries is to provide information useful for the short-term trading of the corresponding company, i.e., to facilitate the inference from news to stock price movement in the next day. We introduce a novel query (i.e., company name) expansion method and a simple unsupervized algorithm for sentence ranking. The system shows promising results in comparison with. | Company-Oriented Extractive Summarization of Financial News Katja Filippova Mihai Surdeanu Massimiliano Ciaramita Hugo Zaragoza EVIL Research gGmbH Yahoo Research Schloss-Wolfsbrunnenweg 33 Avinguda Diagonal 177 69118 Heidelberg Germany 08018 Barcelona Spain filippova@eml-research.de mihai s massi hugoz @yahoo-inc.com Abstract The paper presents a multi-document summarization system which builds companyspecific summaries from a collection of financial news such that the extracted sentences contain novel and relevant information about the corresponding organization. The user s Tamiliarity with the company s profile is assumed. The goal of such summaries is to provide information useful for the short-term trading of the corresponding company i.e. to facilitate the inference from news to stock price movement in the next day. We introduce a novel query i.e. company name expansion method and a simple unsupervized algorithm for sentence ranking. The system shows promising results in comparison with a competitive baseline. 1 Introduction Automatic text summarization has been a field of active research in recent years. While most methods are extractive the implementation details differ considerably depending on the goals of a summarization system. Indeed the intended use of the summaries may help significantly to adapt a particular summarization approach to a specific task whereas the broadly defined goal of preserving relevant although generic information may turn out to be of little use. In this paper we present a system whose goal is to extract sentences from a collection of financial This work was done during the first author s internship at Yahoo Research. Mihai Surdeanu is currently affiliated with Stanford University mihais@stanford.edu . Massimiliano Ciaramita is currently at Google massi@google. com . news to inform about important events concerning companies e.g. to support trading i.e. buy or sell the corresponding symbol on the next day or managing a portfolio.
