In this paper, we propose two generic text summarization methods that create text summaries by ranking and extracting sentences from the original documents. The rst method uses standard IR methods to rank sentence relevances, while the second method uses the latent semantic analysis technique to identify semantically important s e n tences, for summary creations. Both methods strive to select sentences that are highly ranked and di erent from each other. This is an attempt to create a summary with a wider coverage of the document's main content and less redundancy. Performance evaluations on the two summarization methods are conducted by comparing their summarization outputs with the manual summaries generated by three independent h uman evaluators. The evaluations also study the in uence of di erent VSM weighting schemes on the text summarization performances. Finally, the causes of the large disparities in the evaluators' manual summarization results are investigated, and discussions on human text summarization patterns are presented.
Gong REF applies Latent Semantic Analysis (LSA) for summarization.
Generic text summarization using relevance measure and latent semantic analysis
SIGIR '01
Computer Science