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 76–100 of 149 papers

TitleStatusHype
Higher-Order Syntactic Attention Network for Longer Sentence Compression—0
Human acceptability judgements for extractive sentence compression—0
Identification and Characterization of Newsworthy Verbs in World News—0
Identifying Semantic Edit Intentions from Revisions in Wikipedia—0
Improving Multi-documents Summarization by Sentence Compression based on Expanded Constituent Parse Trees—0
Improving Natural Language Processing Tasks with Human Gaze-Guided Neural Attention—0
Improving sentence compression by learning to predict gaze—0
InstructCMP: Length Control in Sentence Compression through Instruction-based Large Language Models—0
Japanese Sentence Compression with a Large Training Dataset—0
Joint Decoding of Tree Transduction Models for Sentence Compression—0
Language as a Latent Variable: Discrete Generative Models for Sentence Compression—0
Large-Scale Paraphrasing for Natural Language Understanding—0
Learning to Generate Coherent Summary with Discriminative Hidden Semi-Markov Model—0
Learning to Summarise Related Sentences—0
Leveraging Information Bottleneck for Scientific Document Summarization—0
Lexico-syntactic text simplification and compression with typed dependencies—0
Looking hard: Eye tracking for detecting grammaticality of automatically compressed sentences—0
LQVSumm: A Corpus of Linguistic Quality Violations in Multi-Document Summarization—0
Machine Translation with Unsupervised Length-Constraints—0
Metaheuristic Approaches to Lexical Substitution and Simplification—0
Monolingual Distributional Similarity for Text-to-Text Generation—0
Multi-document abstractive summarization using ILP based multi-sentence compression—0
Multi-Sentence Compression with Word Vertex-Labeled Graphs and Integer Linear Programming—0
MUSEEC: A Multilingual Text Summarization Tool—0
Neural Clinical Paraphrase Generation with Attention—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