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)

PDF Language: English
Download this file (PDF/138kB)	Preview
Request a format suitable for use with assistive technology e.g. a screenreader
Postscript Language: English
Download this file (Postscript/493kB)	Preview
Request a format suitable for use with assistive technology e.g. a screenreader
Additional URLs: http://citeseerx.ist.psu.edu/viewdoc/sum...

Abstract

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

argumentative structure of the source text. To achieve these goals we use several techniques

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

occurrence of proper nouns, anaphors, discourse markers and a binary-tree representation (due

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,
Institutional Unit:	Schools > School of Computing
Former Institutional Unit:	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:	20 May 2025 10:11 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/21963 (The current URI for this page, for reference purposes)

University of Kent Author Information

Freitas, Alex A..

Creator's ORCID:	https://orcid.org/0000-0001-9825-4700
CReDIT Contributor Roles:

Depositors only (login required):

Total Views

Total unique views of this page since July 2020. For more details click on the image.