Automatic text summarization using a machine learning approach

Neto, J.L. and Freitas, A.A. and Kaestner, Celso A.A. (2003) Automatic text summarization using a machine learning approach. In: Bittencourt, G. and Ramalho, G.L., eds. Advances in Artificial Intelligence. Lecture Notes in Computer Science. Springer-Verlag pp. 205-215. ISBN 978-3-540-00124-9. (The full text of this publication is not available from this repository)

The full text of this publication is not available from this repository. (Contact us about this Publication)
Official URL
http://www.springerlink.com/content/kxjlgtdd7extgj...

Abstract

In this paper we address the automatic summarization task. Recent research works on extractive-summary generation employ some heuristics, but few works indicate how to select the relevant features. We will present a summarization procedure based on the application of trainable Machine Learning algorithms which employs a set of features extracted directly from the original text. These features are of two kinds: statistical - based on the frequency of some elements in the text; and linguistic - extracted from a simplified argumentative structure of the text. We also present some computational results obtained with the application of our summarizer to some well known text databases, and we compare these results to some baseline summarization procedures.

Item Type: Conference or workshop item (Paper)
Uncontrolled keywords: text mining, data mining, machine learning
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Science Technology and Medical Studies > School of Computing > Applied and Interdisciplinary Informatics Group
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 17:59
Last Modified: 18 Jul 2012 08:52
Resource URI: http://kar.kent.ac.uk/id/eprint/13705 (The current URI for this page, for reference purposes)
  • Depositors only (login required):