Sentence Compression
22 papers with code • 1 benchmarks • 2 datasets
Sentence Compression is the task of reducing the length of text by removing non-essential content while preserving important facts and grammaticality.
Benchmarks
These leaderboards are used to track progress in Sentence Compression
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Sentence Compression
Google
Latest papers
SEQ\^3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression
The proposed model does not require parallel text-summary pairs, achieving promising results in unsupervised sentence compression on benchmark datasets.
SEQ^3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression
cbaziotis/seq3
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The proposed model does not require parallel text-summary pairs, achieving promising results in unsupervised sentence compression on benchmark datasets.
Sentence Compression for Arbitrary Languages via Multilingual Pivoting
In this paper we advocate the use of bilingual corpora which are abundantly available for training sentence compression models.
Unsupervised Sentence Compression using Denoising Auto-Encoders
In sentence compression, the task of shortening sentences while retaining the original meaning, models tend to be trained on large corpora containing pairs of verbose and compressed sentences.
Unsupervised Semantic Abstractive Summarization
Automatic abstractive summary generation remains a significant open problem for natural language processing.
Sequence-to-sequence Models for Cache Transition Systems
xiaochang13/CacheTransition-Seq2seq
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ACL 2018
In this paper, we present a sequence-to-sequence based approach for mapping natural language sentences to AMR semantic graphs.
Unsupervised Abstractive Meeting Summarization with Multi-Sentence Compression and Budgeted Submodular Maximization
xcfcode/Summarization-Papers
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ACL 2018
We introduce a novel graph-based framework for abstractive meeting speech summarization that is fully unsupervised and does not rely on any annotations.
Learning How to Simplify From Explicit Labeling of Complex-Simplified Text Pairs
Current research in text simplification has been hampered by two central problems: (i) the small amount of high-quality parallel simplification data available, and (ii) the lack of explicit annotations of simplification operations, such as deletions or substitutions, on existing data.
Combining Graph Degeneracy and Submodularity for Unsupervised Extractive Summarization
We present a fully unsupervised, extractive text summarization system that leverages a submodularity framework introduced by past research.
Sentence Simplification with Deep Reinforcement Learning
XingxingZhang/dress
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EMNLP 2017
Sentence simplification aims to make sentences easier to read and understand.
