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Text Summarization Model based on Maximum Coverage Problem and its Variant
2008
Transactions of the Japanese society for artificial intelligence
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We discuss text summarization in terms of maximum coverage problem and its variant. We explore some decoding algorithms including the ones never used in this summarization formulation, such as a greedy algorithm with performance guarantee, a randomized algorithm, and a branch-andbound method. On the basis of the results of comparative experiments, we also augment the summarization model so that it takes into account the relevance to the document cluster. Through experiments, we showed that the
doi:10.1527/tjsai.23.505
fatcat:v4bph377erfexpyr46ubcws5ki