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Generic text summarization using relevance measure and latent semantic analysis (2001)
| Venue: | in Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval |
| Citations: | 184 - 2 self |
BibTeX
@INPROCEEDINGS{Gong01generictext,
author = {Yihong Gong},
title = {Generic text summarization using relevance measure and latent semantic analysis},
booktitle = {in Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
year = {2001}
}
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Abstract
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 sentences, 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 human 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.
Keyphrases
relevance measure generic text summarization latent semantic analysis generic text summarization method second method summarization method manual summary sentence relevance summary creation text summary di erent wider coverage large disparity rst method original document summarization output main content independent human evaluator performance evaluation text summarization performance important sentence latent semantic analysis technique manual summarization result human text summarization pattern di erent vsm standard ir method
