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
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) |
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| 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: | 05 Nov 2024 10:00 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
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