Abstractive Text Summarization is the task of generating a short and concise summary that captures the salient ideas of the source text. The generated summaries potentially contain new phrases and sentences that may not appear in the source text. Source: Generative Adversarial Network for Abstractive Text Summarization Image credit: Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond
text-summarization
Abstractive Text Summarization
3260 papers • 126 benchmarks • 313 datasets
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Benchmarks
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CNN / Daily Mail
CNN / Daily Mail
Abstractive Text Summarization from Il Post
Abstractive Text Summarization from Il Post
Libraries
iUse these libraries to find text-summarization models and implementations
5 papers 134,677
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Most implemented papers
Attention is All you Need
Noam Shazeer, Ashish Vaswani, Lukasz Kaiser, Jakob Uszkoreit, Niki Parmar, I. Polosukhin, Llion Jones, Aidan N. Gomez•Sun Jun 11 2017
A new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely is proposed, which generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.
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