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Abstractive summarization methods generate new text that did not exist in the original text. [12] 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.
Multi-document summarization is an automatic procedure aimed at extraction of information from multiple texts written about the same topic. The resulting summary report allows individual users, such as professional information consumers, to quickly familiarize themselves with information contained in a large cluster of documents.
The major downside of applying sentence-extraction techniques to the task of summarization is the loss of coherence in the resulting summary. Nevertheless, sentence extraction summaries can give valuable clues to the main points of a document and are frequently sufficiently intelligible to human readers.
This page was last edited on 13 October 2021, at 13:46 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may apply.
A summary is not meant to reproduce the experience of reading or watching the work. In fact, readers might be here because they didn't understand the original. Just repeating what they have already seen or read is unlikely to help them. Do not attempt to re-create the emotional impact of the work through the plot summary.
The topic sentence acts as a kind of summary, and offers the reader an insightful view of the paragraph's main ideas. [3] More than being a mere summary of a paragraph, however, a topic sentence often provides a claim or an insight directly or indirectly related to the thesis. It adds cohesion to an academic text and helps organize ideas not ...
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