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A Trainable Algorithm for Summarizing News Stories

Neto, Joel Larocca, Santos, Alexandre D., Kaestner, Celso A.A., Freitas, Alex A., Nievola, Julio C. (2000) A Trainable Algorithm for Summarizing News Stories. In: Zaragoza, H. and Gallinari, P. and Rajman, M., eds. Proc. PKDD'2000 Workshop on Machine Learning and Textual Information Access. . , Lyon, France (KAR id:21963)

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Abstract

This work proposes a trainable system for summarizing news and obtaining an approximate

and heuristics, such as detecting the main concepts in the text, connectivity between sentences,

to the use of an agglomerative clustering algorithm). The proposed system was evaluated on a

set of 800 documents.

Item Type: Conference or workshop item (Paper)
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Mark Wheadon
Date Deposited: 12 Sep 2009 16:23 UTC
Last Modified: 16 Feb 2021 12:32 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/21963 (The current URI for this page, for reference purposes)
Freitas, Alex A.: https://orcid.org/0000-0001-9825-4700
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