Automatic summarization: Difference between revisions

Revision as of 20:06, 7 March 2023

Revision as of 09:57, 19 May 2023

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===Abstractive-based summarization===

Abstractive summarization methods generate new text that did not exist in the original text.<ref>{{Cite book |last=Zhai |first=ChengXiang |url=https://www.worldcat.org/oclc/957355971 |title=Text data management and analysis : a practical introduction to information retrieval and text mining |date=2016 |others=Sean Massung |isbn=978-1-970001-19-8 |page=321 |___location=[New York, NY] |oclc=957355971}}</ref> This has been applied mainly for text. Abstractive methods build an internal semantic representation of the original content (often called a language model), and then use this representation to create a summary that is closer to what a human might express. Abstraction may transform the extracted content by [[automated paraphrasing|paraphrasing]] sections of the source document, to condense a text more strongly than extraction. Such transformation, however, is computationally much more challenging than extraction, involving both [[natural language processing]] and often a deep understanding of the ___domain of the original text in cases where the original document relates to a special field of knowledge. "Paraphrasing" is even more difficult to apply to images and videos, which is why most summarization systems are extractive.

"Paraphrasing" is even more difficult to apply to image and video, which is why most summarization systems are extractive.

===Aided summarization===