A Joint Sentence Scoring and Selection Framework for Neural Extractive Document Summarization.

IEEE/ACM Transactions on Audio, Speech, and Language Processing(2020)

引用 29|浏览248
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摘要
Extractive document summarization methods aim to extract important sentences to form a summary. Previous works perform this task by first scoring all sentences in the document then selecting most informative ones; while we propose to jointly learn the two steps with a novel end-to-end neural network framework. Specifically, the sentences in the input document are represented as real-valued vectors...
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Bit error rate,Feature extraction,Task analysis,Data mining,Neural networks,History,Training
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