A Trainable Algorithm for Summarizing News Stories

Neto, Joel Larocca and Santos, Alexandre D. and Kaestner, Celso A.A. and Freitas, Alex A. and Nievola, Julio C. (2000) A Trainable Algorithm for Summarizing News Stories. In: Proc. PKDD'2000 Workshop on Machine Learning and Textual Information Access. (Full text available)

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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,
Divisions: Faculties > Sciences > School of Computing > Applied and Interdisciplinary Informatics Group
Depositing User: Mark Wheadon
Date Deposited: 12 Sep 2009 16:23 UTC
Last Modified: 12 Jun 2014 15:54 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/21963 (The current URI for this page, for reference purposes)
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