A learning based model for headline extraction of news articles to find explanatory sentences for events

Sandip Debnath, C. Lee Giles

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

1 Scopus citations

Abstract

Metadata information plays a crucial role in augmenting document organising efficiency and archivability. News metadata includes DateLine, ByLine, HeadLine and many others. We found that HeadLine information is useful for guessing the theme of the news article. Particularly for financial news articles, we found that HeadLine can thus be specially helpful to locate explanatory sentences for any major events such as significant changes in stock prices. In this paper we explore a support vector based learning approach to automatically extract the HeadLine metadata. We find that the classification accuracy of finding the HeadLines improves if DateLines are identified first. We then used the extracted HeadLines to initiate a pattern matching of keywords to find the sentences responsible for story theme. Using this theme and a simple language model it is possible to locate any explanatory sentences for any significant price change.

Original languageEnglish (US)
Title of host publicationProceedings of the 3rd International Conference on Knowledge Capture, K-CAP'05
Pages189-190
Number of pages2
DOIs
StatePublished - Dec 1 2005
Event3rd International Conference on Knowledge Capture, K-CAP'05 - Banff, AB, Canada
Duration: Oct 2 2005Oct 5 2005

Publication series

NameProceedings of the 3rd International Conference on Knowledge Capture, K-CAP'05

Other

Other3rd International Conference on Knowledge Capture, K-CAP'05
CountryCanada
CityBanff, AB
Period10/2/0510/5/05

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Networks and Communications

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    Debnath, S., & Giles, C. L. (2005). A learning based model for headline extraction of news articles to find explanatory sentences for events. In Proceedings of the 3rd International Conference on Knowledge Capture, K-CAP'05 (pp. 189-190). (Proceedings of the 3rd International Conference on Knowledge Capture, K-CAP'05). https://doi.org/10.1145/1088622.1088662