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Author

KARAKAYA, K. Murat; GÜVENIR, H. Altay
Title
ARG: a tool for automatic report generation.
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Abstract
The expansion of on-line text with the rapid growth of the Internet imposes utilizing Data Mining techniques to reveal the information embedded in these documents. Therefore text classification and text summarization are two of the most important application areas. In this work, we attempt to integrate these two techniques to help the user to compile and extract the information that is needed. Basically, we propose a two-phase algorithm in which the paragraphs in the documents are first classified according to given topics and then each topic is summarized to constitute the automatically generated report (AU)
Keywords
Data mining; text summarization; text classification; automatic report generation
Assessment

Author

KAY, Roderick; AYLETT, Ruth
Title
Transitivity and foregrounding in news articles: experiments in information retrieval and automatic summarising
Source
34th Annual Meeting of the Association for Computational Linguistics, 1996
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PDF
Abstract
This paper describes an on-going study which applies the concept of transitivity to news discourse for text processing tasks. The complex notion of transitivity is defined and the relationship between transitivity and information foregrounding is explained. A sample corpus of news articles has been coded for transitivity. The corpus is being used in two text processing experiments (AU)
Keywords
transitivity; text processing
Assessment

Author

Knoweldge Acquisition & Machine Learning Research Group
Title
The Text Summarization Project
Source
Université d’Ottawa. Knowledge Acquisition & Machine Learning Research Group, 1996-2001
Support
On line ( 15/06/2004)
Abstract
Presentation of The Text Summarization Project and the team in charge, with active links to websites, conferences, articles, books…
Keywords
Summarization
Assessment

Author

LAL, Partha; RUEGER, Stefan
Title
Extract-based Summarization with Simplification
Source
DUC 2002, Workshop on Text Summarization
Support
PDF
Abstract
We describe a single-document text summarizer using the Text Engineering framework GATE. The summariser extracts sentences using a combination of simple Bayes classifiers, resolves anaphora using GATE's ANNIE module, simplifies words using the MRC psycho-linguistic database and WordNet, and supplies background information to named persons and places using internet resources (AU)
Keywords
GATE; summarizer; ANNIE module; MRC psycho-linguistic database; WordNet
Assessment
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