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Extractive Document Summarization On Cnn

Métriques

ROUGE-1

ROUGE-2

ROUGE-L

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	ROUGE-1	ROUGE-2	ROUGE-L	Paper Title	Repository
REFRESH	40.0	18.2	36.6	Ranking Sentences for Extractive Summarization with Reinforcement Learning	-
HIBERT	42.37	19.95	38.83	HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization	-
Lead-3 baseline	40.34	17.70	36.57	Get To The Point: Summarization with Pointer-Generator Networks	-
NeuSUM	41.59	19.01	37.98	Neural Document Summarization by Jointly Learning to Score and Select Sentences	-
A2Summ	44.11	20.31	35.92	Align and Attend: Multimodal Summarization with Dual Contrastive Losses	-
Latent	41.05	18.77	37.54	Neural Latent Extractive Document Summarization	-
ITS	30.80	12.6	-	Iterative Document Representation Learning Towards Summarization with Polishing	-
PNBERT	42.69	19.60	38.85	Searching for Effective Neural Extractive Summarization: What Works and What's Next	-
BERT-ext + RL	42.76	19.87	39.11	Summary Level Training of Sentence Rewriting for Abstractive Summarization	-
BanditSum	41.5	18.7	37.6	BanditSum: Extractive Summarization as a Contextual Bandit	-
NeRoBERTa	43.86	20.64	40.20	Considering Nested Tree Structure in Sentence Extractive Summarization with Pre-trained Transformer	-
MatchSum	44.41	20.86	40.55	Extractive Summarization as Text Matching	-
HAHSum	44.68	21.30	40.75	Neural Extractive Summarization with Hierarchical Attentive Heterogeneous Graph Network	-
HER	42.3	18.9	37.9	Reading Like HER: Human Reading Inspired Extractive Summarization

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Extractive Document Summarization On Cnn | SOTA | HyperAI