natural-language-processing-10
Summarization
3260 papers • 126 benchmarks • 313 datasets
Summarization is the task of producing a shorter version of one or several documents that preserves most of the input's meaning.
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MuLD (VLSP)
MuLD (VLSP)
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Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond
Çaglar Gülçehre, Ramesh Nallapati, Bowen Zhou, C. D. Santos, Bing Xiang•Thu Feb 18 2016
This work proposes several novel models that address critical problems in summarization that are not adequately modeled by the basic architecture, such as modeling key-words, capturing the hierarchy of sentence-to-word structure, and emitting words that are rare or unseen at training time.
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