Abstractive Text Summarization On Cnn Daily

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ROUGE-1

ROUGE-2

ROUGE-L

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	ROUGE-1	ROUGE-2	ROUGE-L	Paper Title	Repository
end2end w/ inconsistency loss	40.68	17.97	37.13	A Unified Model for Extractive and Abstractive Summarization using Inconsistency Loss
Subformer-base	40.9	18.3	37.7	Subformer: A Parameter Reduced Transformer	-
Two-Stage + RL	41.71	19.49	38.79	Pretraining-Based Natural Language Generation for Text Summarization
PEGASUS	44.17	21.47	41.11	PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
Selector & Pointer-Generator	41.72	18.74	38.79	Mixture Content Selection for Diverse Sequence Generation
DELTA (BLSTM)	-	-	27.3	DELTA: A DEep learning based Language Technology plAtform
PTGEN + Coverage	39.53	17.28	36.38	Get To The Point: Summarization with Pointer-Generator Networks
SRformer-BART	43.19	19.80	40.40	Segmented Recurrent Transformer: An Efficient Sequence-to-Sequence Model
CoCoNet + CoCoPretrain	44.50	21.55	41.24	Learn to Copy from the Copying History: Correlational Copy Network for Abstractive Summarization	-
Scrambled code + broken	46.71	20.39	43.56	Universal Evasion Attacks on Summarization Scoring
ERNIE-GENLARGE (large-scale text corpora)	44.31	21.35	41.60	ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation
Transformer	39.50	16.06	36.63	Attention Is All You Need
MUPPET BART Large	44.45	21.25	41.4	Muppet: Massive Multi-task Representations with Pre-Finetuning
RL + pg + cbdec	40.66	17.87	37.06	Closed-Book Training to Improve Summarization Encoder Memory	-
Summary Loop Unsup	37.7	-	-	The Summary Loop: Learning to Write Abstractive Summaries Without Examples
GLM-XXLarge	44.7	21.4	41.4	GLM: General Language Model Pretraining with Autoregressive Blank Infilling
ERNIE-GENBASE	42.30	19.92	39.68	ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation
BertSumExtAbs	42.13	19.6	39.18	Text Summarization with Pretrained Encoders
Li et al.	40.30	18.02	37.36	Improving Neural Abstractive Document Summarization with Structural Regularization	-
PTGEN + Coverage	39.53	17.28	36.38	Get To The Point: Summarization with Pointer-Generator Networks

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