Company-Oriented Extractive Summarization of Financial News

Proceedings of the 12th Conference of the European Chapter of the ACL, pages 246–254, Athens, Greece, 30 March – 3 April 2009. c⃝2009 Association for Computational Linguistics Company-Oriented Extractive Summarization of Financial News∗ Katja Filippova†, Mihai Surdeanu‡, Massimiliano Ciaramita‡, Hugo Zaragoza‡ †EML Research gGmbH ‡Yahoo! Research Schloss-Wolfsbrunnenweg 33 Avinguda Diagonal 177 69118 Heidelberg, Germany 08018 Barcelona, Spain filippova@eml-research.de,{mihais,massi,hugoz}@yahoo-inc.com Abstract The paper presents a multi-document sum- marization system which builds company- specific summaries from a collection of fi- nancial news such that the extracted sen- tences contain novel and relevant infor- mation about the corresponding organiza- tion. The user’s familiarity with the com- pany’s profile is assumed. The goal of such summaries is to provide information useful for the short-term trading of the cor- responding company, i.e., to facilitate the inference from news to stock price move- ment in the next day. We introduce a novel query (i.e., company name) expan- sion method and a simple unsupervized al- gorithm for sentence ranking. The sys- tem shows promising results in compari- son with a competitive baseline. 1 Introduction Automatic text summarization has been a field of active research in recent years. While most meth- ods are extractive, the implementation details dif- fer considerably depending on the goals of a sum- marization system. Indeed, the intended use of the summaries may help significantly to adapt a par- ticular summarization approach to a specific task whereas the broadly defined goal of preserving rel- evant, 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 concern- ing companies, e.g., to support trading (i.e., buy or sell) the corresponding symbol on the next day, or managing a portfolio. For example, a company’s announcement of surpassing its earnings’ estimate is likely to have a positive short-term effect on its stock price, whereas an announcement of job cuts is likely to have the reverse effect. We demonstrate how existing methods can be extended to achieve precisely this goal. In a way, the described task can be classified as query-oriented multi-document summarization because we are mainly interested in information related to the company and its sector. However, there are also important differences between the two tasks. • The name of the company is not a query, e.g., as it is specified in the context of the DUC competitions1, and requires an exten- sion. Initially, a query consists exclusively of the “symbol”, i.e., the abbreviation of the name of a company as it is listed on the stock market. For example, WPO is the abbrevia- tion used on the stock market to refer to The Washington Post–a large media and educa- tion company. Such symbols are rarely en- countered in the news and cannot be used to …

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