Company-oriented extractive summarization of financial news

Katja Filippova, Mihai Surdeanu, Massimiliano Ciaramita, Hugo Zaragoza

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

The paper presents a multi-document summarization system which builds company-specific 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 a competitive baseline.

Original languageEnglish (US)
Title of host publicationEACL 2009 - 12th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings
PublisherAssociation for Computational Linguistics (ACL)
Pages246-254
Number of pages9
ISBN (Print)9781932432169
DOIs
StatePublished - 2009
Event12th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2009 - Athens, Greece
Duration: Mar 30 2009Apr 3 2009

Publication series

NameEACL 2009 - 12th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings

Other

Other12th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2009
CountryGreece
CityAthens
Period3/30/094/3/09

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

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