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Automatic text summarization using a machine learning approach

Neto, Joel Larocca and Freitas, Alex A. and Kaestner, Celso A.A. (2003) Automatic text summarization using a machine learning approach. In: Bittencourt, Guilherme and Ramalho, Geber L., eds. Advances in Artificial Intelligence 16th Brazilian Symposium on Artificial Intelligence. Lecture Notes in Computer Science . Springer, pp. 205-215. ISBN 978-3-540-00124-9. E-ISBN 978-3-540-36127-5. (doi:10.1007/3-540-36127-8_20) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:13705)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1007/3-540-36127-8_20

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: Book section
DOI/Identification number: 10.1007/3-540-36127-8_20
Uncontrolled keywords: text mining, data mining, machine learning
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: 24 Nov 2008 17:59 UTC
Last Modified: 16 Feb 2021 12:24 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/13705 (The current URI for this page, for reference purposes)
Freitas, Alex A.: https://orcid.org/0000-0001-9825-4700
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