Who Cited It

Get To The Point: Summarization with Pointer-Generator Networks

2017 · 3,935 citations · 3 from inside this corpus

Abigail See, Peter J. Liu, Christopher D. Manning

Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text). However, these models have two shortcomings: they are liable to reproduce factual details inaccurately, and they tend to repeat themselves. In this work we propose a novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways. First, we use a hybrid pointer-generator network that can copy words from the source text via pointing, which aids accurate reproduction of information, while retaining the ability to produce novel words through the generator. Second, we use coverage to keep track of what has been summarized, which discourages repetition. We apply our model to the CNN / Daily Mail summarization task, outperforming the current abstractive state-of-the-art by at least 2 ROUGE points.

Get To The Point: Summarization with Pointer-Generator Networks (2017)Get To The Point: Summarizati…Neural Machine Translation by Jointly Learning to Align and Translate (2014)Neural Machine Translation by…Sequence to Sequence Learning with Neural Networks (2014)Sequence to Sequence Learning…Adaptive Subgradient Methods for Online Learning and Stochastic Optimization (2010)Adaptive Subgradient Methods …ROUGE: A Package for Automatic Evaluation of Summaries (2004)ROUGE: A Package for Automati…Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond (2016)Abstractive Text Summarizatio…Meteor Universal: Language Specific Translation Evaluation for Any Target Language (2014)Meteor Universal: Language Sp…Teaching Machines to Read and Comprehend (2015)Teaching Machines to Read and…Teaching Machines to Read and Comprehend (2015)Teaching Machines to Read and…Incorporating Copying Mechanism in Sequence-to-Sequence Learning (2016)Incorporating Copying Mechani…Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (2019)Exploring the Limits of Trans…fairseq: A Fast, Extensible Toolkit for Sequence Modeling (2019)fairseq: A Fast, Extensible T…Text Summarization with Pretrained Encoders (2019)Text Summarization with Pretr…
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What this paper cites, inside the corpus

PaperYearCited
Neural Machine Translation by Jointly Learning to Align and Translate201414,615
Sequence to Sequence Learning with Neural Networks201413,351
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization20108,621
ROUGE: A Package for Automatic Evaluation of Summaries20048,302
Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond20162,233
Meteor Universal: Language Specific Translation Evaluation for Any Target Language20142,090
Teaching Machines to Read and Comprehend20151,939
Teaching Machines to Read and Comprehend20151,519
Incorporating Copying Mechanism in Sequence-to-Sequence Learning20161,442

What cites it, inside the corpus

PaperYearCited
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer20193,698
fairseq: A Fast, Extensible Toolkit for Sequence Modeling20192,511
Text Summarization with Pretrained Encoders20191,633

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Topics

Topic ModelingComputer Science
Natural Language Processing TechniquesComputer Science
Speech Recognition and SynthesisComputer Science

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