Text Summarization On Pubmed 1

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ROUGE-1

ROUGE-2

ROUGE-L

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이 벤치마크에서 각 모델의 성능 결과

				Paper Title
Top Down Transformer (AdaPool) (464M)	51.05	23.26	46.47	Long Document Summarization with Top-down and Bottom-up Inference
eyeglaxs	50.34	24.57	45.96	Scaling Up Summarization: Leveraging Large Language Models for Long Text Extractive Summarization
BART-LS	50.3	-	-	Adapting Pretrained Text-to-Text Models for Long Text Sequences
LongT5	50.23	24.76	46.67	LongT5: Efficient Text-To-Text Transformer for Long Sequences
GoSum (extractive)	49.83	23.56	45.10	GoSum: Extractive Summarization of Long Documents by Reinforcement Learning and Graph Organized discourse state
Lodoss-full-large (extractive)	49.38	23.89	44.84	Toward Unifying Text Segmentation and Long Document Summarization
MemSum (extractive)	49.25	22.94	44.42	MemSum: Extractive Summarization of Long Documents Using Multi-Step Episodic Markov Decision Processes
Lodoss-full-base (extractive)	48.93	23.51	44.40	Toward Unifying Text Segmentation and Long Document Summarization
HAT-BART	48.25	21.35	36.69	Hierarchical Learning for Generation with Long Source Sequences
GRETEL	48.20	21.20	43.16	GRETEL: Graph Contrastive Topic Enhanced Language Model for Long Document Extractive Summarization
DeepPyramidion	47.81	21.14	-	Sparsifying Transformer Models with Trainable Representation Pooling
FactorSum	47.5	20.33	43.76	Factorizing Content and Budget Decisions in Abstractive Summarization of Long Documents
HiStruct+	46.59	20.39	42.11	HiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Information
DANCER PEGASUS	46.34	19.97	42.42	A Divide-and-Conquer Approach to the Summarization of Long Documents
BigBird-Pegasus	46.32	20.65	42.33	Big Bird: Transformers for Longer Sequences
ExtSum-LG+MMR-Select+	45.39	20.37	40.99	Systematically Exploring Redundancy Reduction in Summarizing Long Documents
ExtSum-LG+RdLoss	45.3	20.42	40.95	Systematically Exploring Redundancy Reduction in Summarizing Long Documents
PEGASUS	45.09	-	-	PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
Sent-CLF	45.01	-	-	On Extractive and Abstractive Neural Document Summarization with Transformer Language Models
ExtSum-LG	44.81	19.74	-	Extractive Summarization of Long Documents by Combining Global and Local Context

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Text Summarization On Pubmed 1

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