Generating Text Summaries through the Relative Importance of Topics

Neto, Joel Larocca and Santos, Alexandre D. and Kaestner, Celso A.A. and Freitas, Alex A. (2000) Generating Text Summaries through the Relative Importance of Topics. In: Proc. Int. Joint Conf. IBERAMIA-2000 (7th Ibero-American Conf. on Artif. Intel.) and SBIA-2000 (15th Brazilian Symp. on Artif. Intel.). Lecture Notes in Artificial Intelligence, 1952. Springer-Verlag, Atibaia, SP, Brazil pp. 301-309. ISBN 3-540-41276-X. (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)

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This work proposes a new extractive text-summarization algorithm based on the importance of the topics contained in a document. The basic ideas of the proposed algorithm are as follows. At first the document is partitioned by using the TextTiling algorithm, which identifies topics (coherent segments of text) based on the TF-IDF metric. Then for each topic the algorithm computes a measure of its relative relevance in the document. This measure is computed by using the notion of TF-ISF (Term Frequency - Inverse Sentence Frequency), which is our adaptation of the well-known TF-IDF (Term Frequency - Inverse Document Frequency) measure in information retrieval. Finally, the summary is generated by selecting from each topic a number of sentences proportional to the importance of that topic.

Item Type: Conference or workshop item (Paper)
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: 13 Sep 2009 20:01
Last Modified: 16 Apr 2014 08:43
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