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A good space: Lexical predictors in vector space evaluation
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BibTeX
@MISC{Smith_agood,
author = {Christian Smith and Henrik Danielsson and Arne Jönsson},
title = {A good space: Lexical predictors in vector space evaluation},
year = {}
}
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Abstract
Vector space models benefit from using an outside corpus to train the model. It is, however, unclear what constitutes a good training corpus. We have investigated the effect on summary quality when using various language resources to train a vector space based extraction summarizer. This is done by evaluating the performance of the summarizer utilizing vector spaces built from corpora from different genres, partitioned from the Swedish SUC-corpus. The corpora are also characterized using a variety of lexical measures commonly used in readability studies. The performance of the summarizer is measured by comparing automatically produced summaries to human created gold standard summaries using the ROUGE F-score. Our results show that the genre of the training corpus does not have a significant effect on summary quality. However, evaluating the variance in the F-score between the genres based on lexical measures as independent variables in a linear regression model, shows that vector spaces created from texts with high syntactic complexity, high word variation, short sentences and few long words produce better summaries.
Keyphrases
vector space evaluation lexical predictor good space vector space summary quality lexical measure high word variation rouge f-score significant effect high syntactic complexity extraction summarizer linear regression model long word vector space model gold standard summary short sentence independent variable different genre swedish suc-corpus outside corpus readability study various language resource good training corpus summarizer utilizing vector space training corpus
