Document Summarization On Cnn Daily Mail

Métriques

PPL

ROUGE-1

ROUGE-2

ROUGE-L

Résultats

Résultats de performance de divers modèles sur ce benchmark

					Paper Title
Scrambled code + broken (alter)	-	48.18	19.84	45.35	Universal Evasion Attacks on Summarization Scoring
PEGASUS + SummaReranker	-	47.16	22.55	43.87	SummaReranker: A Multi-Task Mixture-of-Experts Re-ranking Framework for Abstractive Summarization
Fourier Transformer	-	44.76	21.55	41.34	Fourier Transformer: Fast Long Range Modeling by Removing Sequence Redundancy with FFT Operator
GLM-XXLarge	-	44.7	21.4	41.4	GLM: General Language Model Pretraining with Autoregressive Blank Infilling
HAT-BART	-	44.48	21.31	41.52	Hierarchical Learning for Generation with Long Source Sequences
MatchSum (RoBERTa-base)	-	44.41	20.86	40.55	Extractive Summarization as Text Matching
Hie-BART	-	44.35	21.37	41.05	Hie-BART: Document Summarization with Hierarchical BART
MatchSum (BERT-base)	-	44.22	20.62	40.38	Extractive Summarization as Text Matching
BertSumExt	-	43.85	20.34	39.9	Text Summarization with Pretrained Encoders
BigBird-Pegasus	-	43.84	21.11	40.74	Big Bird: Transformers for Longer Sequences
T5-11B	-	43.52	21.55	40.69	Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
BERTSUM+Transformer	-	43.25	20.24	39.63	Fine-tune BERT for Extractive Summarization
UniLM (Abstractive Summarization)	-	43.08	20.43	40.34	Unified Language Model Pre-training for Natural Language Understanding and Generation
Selector+Pointer Generator	-	41.72	18.74	38.79	Mixture Content Selection for Diverse Sequence Generation
NeuSUM	-	41.59	19.01	37.98	Neural Document Summarization by Jointly Learning to Score and Select Sentences
Bottom-Up Sum	32.75	41.22	18.68	38.34	Bottom-Up Abstractive Summarization
TaLK Convolutions (Deep)	-	40.59	18.97	36.81	Time-aware Large Kernel Convolutions
Lead-3	-	40.34	17.70	36.57	Get To The Point: Summarization with Pointer-Generator Networks
TaLK Convolutions (Standard)	-	40.03	18.45	36.13	Time-aware Large Kernel Convolutions
ML + RL (Paulus et al., 2017)	-	39.87	15.82	36.90	A Deep Reinforced Model for Abstractive Summarization

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Document Summarization On Cnn Daily Mail

Métriques

Résultats