BibTeX
@MISC{For03usinglatent,
author = {Tristan Miller For and Tristan Miller and Tristan Miller},
title = {Using Latent Semantic Analysis},
year = {2003}
}
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Abstract
Using Latent Semantic Analysis Tristan Miller Master of Science Graduate Department of Computer Science University of Toronto 2003 A major problem with automatically-produced summaries in general, and extracts in particular, is that the output text often lacks textual coherence. Our goal is to improve the textual coherence of automatically produced extracts. We developed and implemented an algorithm which builds an initial extract composed solely of topic sentences, and then recursively lls in the lacunae by providing linking material from the original text between semantically dissimilar sentences. Our summarizer diers in architecture from most others in that it measures semantic similarity with latent semantic analysis (LSA), a factor analysis technique based on the vector-space model of information retrieval. We believed that the deep semantic relations discovered by LSA would assist in the identi cation and correction of abrupt topic shifts in the summaries. However, our experiments did not show a statistically signi cant dierence in the coherence of summaries produced by our system as compared with a non-LSA version.
Keyphrases
latent semantic analysis textual coherence summarizer diers initial extract science graduate department signi cant dierence abrupt topic shift dissimilar sentence topic sentence semantic similarity information retrieval factor analysis technique identi cation non-lsa version deep semantic relation automatically-produced summary vector-space model original text computer science university major problem
