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Sentence Compression
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Sentence Compression is the task of reducing the length of text by removing non-essential content while preserving important facts and grammaticality.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Target-Aware Language Modeling via Granular Data Sampling
InstructCMP: Length Control in Sentence Compression through Instruction-based Large Language Models
AutoBreach: Universal and Adaptive Jailbreaking with Efficient Wordplay-Guided Optimization
From Lengthy to Lucid: A Systematic Literature Review on NLP Techniques for Taming Long Sentences
Reconstruct Before Summarize: An Efficient Two-Step Framework for Condensing and Summarizing Meeting Transcripts
Improving Factual Consistency in Summarization with Compression-Based Post-Editing
Unsupervised Abstractive Dialogue Summarization with Word Graphs and POV Conversion
Efficient Unsupervised Sentence Compression by Fine-tuning Transformers with Reinforcement Learning
Contextualized Semantic Distance between Highly Overlapped Texts
Leveraging Information Bottleneck for Scientific Document Summarization
Cross-Register Projection for Headline Part of Speech Tagging
Non-Autoregressive Text Generation with Pre-trained Language Models
With Measured Words: Simple Sentence Selection for Black-Box Optimization of Sentence Compression Algorithms
Evaluation Discrepancy Discovery: A Sentence Compression Case-study
Improving Natural Language Processing Tasks with Human Gaze-Guided Neural Attention
A Token-wise CNN-based Method for Sentence Compression
Automatic Speech Summarisation: A Scoping Review
Sentence Compression as Deletion with Contextual Embeddings
Combining Word Embeddings and N-grams for Unsupervised Document Summarization
A Multilingual Study of Multi-Sentence Compression using Word Vertex-Labeled Graphs and Integer Linear Programming
Machine Translation with Unsupervised Length-Constraints
Syntactically Look-Ahead Attention Network for Sentence Compression
A Difference-of-Convex Programming Approach With Parallel Branch-and-Bound For Sentence Compression Via A Hybrid Extractive Model
Explicit Sentence Compression for Neural Machine Translation
Towards Annotating and Creating Sub-Sentence Summary Highlights
Deleter: Leveraging BERT to Perform Unsupervised Successive Text Compression
Query-focused Sentence Compression in Linear Time
SEQ^3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression
Sentence Compression via DC Programming Approach
Human acceptability judgements for extractive sentence compression
SALSA-TEXT : self attentive latent space based adversarial text generation
Text Summarization as Tree Transduction by Top-Down TreeLSTM
Unsupervised Sentence Compression using Denoising Auto-Encoders
Adversarial Neural Networks for Cross-lingual Sequence Tagging
Unsupervised Abstractive Meeting Summarization with Multi-Sentence Compression and Budgeted Submodular Maximization
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