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pSum-SaDE: A Modified p-Median Problem and Self-Adaptive ...
MMI (Maximal Marginal Importance) is also a diversity-based text summarization method for summary generation. It depends on the extraction of the most important sentences from the original text. It depends on the extraction of the most important sentences from the original text.
 Extractive Summarization using Deep Learning
Text Summarization can be classi ed into extractive summarization and ab-stractive summarization based on the summary generated. Extractive summa-rization is creating a summary based on strictly what you get in the original text. Abstractive summarization mimics the process of paraphrasing a text. Text(s)...
Google AI Blog: Text summarization with TensorFlow
Extractive and Abstractive summarization One approach to summarization is to extract parts of the document that are deemed interesting by some metric (for example, inverse-document frequency) and join them to form a summary. Algorithms of this flavor are called extractive summarization. Original Text: Alice and Bob took the train to visit the zoo.
[PDF] Unsupervised Summarization by Jointly Extracting ...
We present RepRank, an unsupervised graphbased ranking model for extractive multidocument summarization in which the similarity between words, sentences, and word-tosentence can be estimated by the distances between their vector representations in a unified vector space. In order to obtain desirable representations, we propose a self-attention based learning method that represent a sentence by ...
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