Abstract
With the rapid popularization of the Internet and the overexpansion of information, it is an urgent need for a text compression technology to extract condensed information for readers. Automatic text summarization, also known as document summarization, is such an information compression technology that automatically converts document (or a collection of documents) into a short summary. This chapter first introduces the main tasks in automatic text summarization, and then details different approaches to automatic summarization technology for single-document and multidocument summarization. After that, we introduce query-based summarization and crosslingual and multilingual summarization. Finally, we introduce the evaluation methods for automatic text summarization.
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Notes
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Concepts and facts have different definitions. Generally, concepts correspond to entities (people, objects, institutions, etc.) and facts correspond to actions.
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In practical application, the scope of candidates for concepts and facts can be expanded or contracted appropriately according to specific needs.
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For example, the open-source toolkit released by Stanford University can perform coreference resolution for English entities. https://nlp.stanford.edu/projects/coref.shtml.
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Zong, C., Xia, R., Zhang, J. (2021). Automatic Text Summarization. In: Text Data Mining. Springer, Singapore. https://doi.org/10.1007/978-981-16-0100-2_11
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