SOTAVerified

Sentence Compression

Sentence Compression is the task of reducing the length of text by removing non-essential content while preserving important facts and grammaticality.

Papers

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Showing 21–30 of 149 papers

TitleStatusHype
SEQ^3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence CompressionCode0
SCAR: Sentence Compression using Autoencoders for ReconstructionCode0
An Operation Network for Abstractive Sentence Compression—0
Annotating and Predicting Non-Restrictive Noun Phrase Modifications—0
Adversarial Neural Networks for Cross-lingual Sequence Tagging—0
Automatic Speech Summarisation: A Scoping Review—0
Bayesian Symbol-Refined Tree Substitution Grammars for Syntactic Parsing—0
Can Syntax Help? Improving an LSTM-based Sentence Compression Model for New Domains—0
Cascaded Attention based Unsupervised Information Distillation for Compressive Summarization—0
Automatic Grammatical Error Correction for Sequence-to-sequence Text Generation: An Empirical Study—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SLAHAN (LSTM+syntactic-information)F10.86—Unverified
2BiRNN + LM EvaluatorF10.85—Unverified
3Higher-Order Syntactic Attention NetworkF10.84—Unverified
4LSTMF10.82—Unverified
5LSTMs + eye-movementF10.81—Unverified
6BiLSTMF10.8—Unverified