REVIEW 2 cited by
Global Encoding for Abstractive Summarization
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
In neural abstractive summarization, the conventional sequence-to-sequence (seq2seq) model often suffers from repetition and semantic irrelevance. To tackle the problem, we propose a global encoding framework, which controls the information flow from the encoder to the decoder based on the global information of the source context. It consists of a convolutional gated unit to perform global encoding to improve the representations of the source-side information. Evaluations on the LCSTS and the English Gigaword both demonstrate that our model outperforms the baseline models, and the analysis shows that our model is capable of reducing repetition.
Forward citations
Cited by 2 Pith papers
-
Deep Neural Network for Semantic-based Text Recognition in Images
A context-aware text recognition pipeline that groups and orders words in images and then applies a sequence-to-sequence correction model achieves 90% word accuracy on catalog images and 71% on protest sign images, be...
-
Denoising based Sequence-to-Sequence Pre-training for Text Generation
PoDA pre-trains a Transformer plus pointer-generator seq2seq model as a denoising autoencoder and reports gains over non-pre-trained baselines on summarization and grammatical error correction.
Discussion (0). Continue with ORCID to comment.