Automatic Single-Document Key Fact Extraction from Newswire Articles

Proceedings of the 12th Conference of the European Chapter of the ACL, pages 415–423, Athens, Greece, 30 March – 3 April 2009. c⃝2009 Association for Computational Linguistics Automatic Single-Document Key Fact Extraction from Newswire Articles Itamar Kastner Department of Computer Science Queen Mary, University of London, UK itk1@dcs.qmul.ac.uk Christof Monz ISLA, University of Amsterdam Amsterdam, The Netherlands christof@science.uva.nl Abstract This paper addresses the problem of ex- tracting the most important facts from a news article. Our approach uses syntac- tic, semantic, and general statistical fea- tures to identify the most important sen- tences in a document. The importance of the individual features is estimated us- ing generalized iterative scaling methods trained on an annotated newswire corpus. The performance of our approach is evalu- ated against 300 unseen news articles and shows that use of these features results in statistically significant improvements over a provenly robust baseline, as measured using metrics such as precision, recall and ROUGE. 1 Introduction The increasing amount of information that is avail- able to both professional users (such as journal- ists, financial analysts and intelligence analysts) and lay users has called for methods condensing information, in order to make the most important content stand out. Several methods have been pro- posed over the last two decades, among which keyword extraction and summarization are the most prominent ones. Keyword extraction aims to identify the most relevant words or phrases in a document, e.g., (Witten et al., 1999), while sum- marization aims to provide a short (commonly 100 words), coherent full-text summary of the docu- ment, e.g., (McKeown et al., 1999). Key fact ex- traction falls in between key word extraction and summarization. Here, the challenge is to identify the most relevant facts in a document, but not nec- essarily in a coherent full-text form as is done in summarization. Evidence of the usefulness of key fact extraction is CNN’s web site which since 2006 has most of its news articles preceded by a list of story highlights, see Figure 1. The advantage of the news highlights as opposed to full-text summaries is that they are much ‘easier on the eye’ and are better suited for quick skimming. So far, only CNN.com offers this service and we are interested in finding out to what extent it can be automated and thus applied to any newswire source. Although these highlights could be eas- ily generated by the respective journalists, many news organization shy away from introducing an additional manual stage into the workflow, where pushback times of minutes are considered unac- ceptable in an extremely competitive news busi- ness which competes in terms of seconds rather than minutes. Automating highlight generation can help eliminate those delays. Journalistic training emphasizes that news arti- cles should contain the most important informa- tion in the beginning, while less important infor- mation, such as background or additional details, appears further down in the article. This is also the main reason why most summarization systems applied to news articles do not outperform a sim- …

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