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Text Summarization on Gigaword (test)

46.99ROUGE-1EncDec+DOC
14.417222.873631.3339.7864Sep 2, 2015Oct 16, 2016Nov 30, 2017Jan 14, 2019Feb 28, 2020Apr 13, 2021May 28, 2022

Evaluation results

MethodLinks
EncDec+DOCi3=2, i2=22018.0846.9925.2943.83——
EncDec+DOCi3=22018.0846.9124.9143.73——
SEASS2018.0846.8624.5843.53——
EncDec2018.0846.7724.8743.58——
Kiyono et al.2018.0846.3424.8543.49——
BARTModel scale=base2021.0139.3420.0741.25——
PEGASUSSupervision level=Fully-supervised2022.0539.1219.8636.24——
T5Model scale=base2021.0138.8319.6840.76——
ProphetNetModel scale=large2021.0138.4918.4139.84——
Liu & PanEncoder=4-layer bidirectional LSTM, Decoder=1-layer LSTM, Attention=Softmax attention2017.0438.2218.735.74——
BERT2BERTModel scale=base2021.0138.1618.8940.06——
Soft Monotonic AttentionEncoder=4-layer bidirectional LSTM, Decoder=1-layer LSTM2017.0438.0318.5735.7——
ABS2018.0837.4115.8734.7——
FTSumgContext Handling=Gating, priorities fact descriptions2017.1137.2717.6534.24——
Hard Monotonic AttentionEncoder=4-layer bidirectional LSTM, Decoder=1-layer LSTM2017.0437.141834.87——
RNN+Attn2021.0136.3217.6338.36——
Suzuki & Nagata2017.0436.317.3133.88——
Transformer2021.0136.2117.6438.1——
SEASSDecoding Strategy=beam2017.0436.1517.5433.63——
FTSumcContext Handling=Treats source sentence and relations equally2017.1135.7316.0234.13——
SEASSDecoding Strategy=greedy2017.0435.4816.532.93——
words-lvt5k-1sentEvaluation protocol=Full length F1, Vocabulary size=5k, Summary length=1 sentence2016.0235.316.6432.62——
Yu et al. 2016a2017.0434.4116.8631.83——
s2s+attArchitecture=Standard attentional s2s, Framework=dl4mt2017.1134.2315.5231.57——
Nallapati et al.2017.0434.1916.2932.13——
Miao & Blunsom2017.0434.1715.9431.92——
s2s+attDecoding Strategy=beam2017.0434.0415.9531.68——
RAS-ElmanEvaluation protocol=Full length F12016.0233.7815.9731.15——
Chopra et al.2017.0433.7815.9731.15——
CAs2sDecoding Strategy=beam2017.0433.7815.9731.15——
RAS-ElmanEncoder=Convolutional attention-based, Decoder=RNN2017.1133.7815.9731.15——
NACC (length control)Setting=Supervised, Model Category=NAR, Len (Avg chars)=34.4, Time (s)=0.0172022.0533.6613.7331.79—4.74
s2s+attDecoding Strategy=greedy2017.0433.1814.7930.8——
NACC (truncate)Setting=Supervised, Model Category=NAR, Len (Avg chars)=34.15, Time (s)=0.0112022.0533.1213.9331.34—1.34
CAs2sDecoding Strategy=greedy2017.0433.114.4530.25——
Luong-NMTDecoding Strategy=beam2017.0433.114.4530.71——
Luong-NMTArchitecture=Two-layer LSTMs, Hidden units=5002017.1133.114.4530.71——
words-lvt2k-1sentEvaluation protocol=Full length F1, Vocabulary size=2k, Summary length=1 sentence2016.0232.6715.5930.6474.57—
Feats2sDecoding Strategy=beam2017.0432.6715.5930.64——
Feats2sArchitecture=s2s RNN, Features=POS tag, NER2017.1132.6715.5930.64——
Su et al. (truncate)Setting=Supervised, Model Category=NAR, Len (Avg chars)=38.43, Time (s)=0.0162022.0532.2814.2130.56—0
Qi et al. (truncate)Setting=Supervised, Model Category=NAR, Len (Avg chars)=27.98, Time (s)=0.0192022.0531.6912.5230.05—-2.79
ABS+type=Attention-Based Summarization, tuning=MERT tuning step (Z-MERT)2015.093112.6528.3491.5—
ABStype=Attention-Based Summarization2015.0930.8812.2227.7785.4—
Yu et al. 2016b2017.0430.2713.6827.91——
ABS+Evaluation protocol=Full length F12016.0229.7811.8926.9791.5—
ABS+Encoder=Attentive CNN, Decoder=NNLM, Features=Hand-crafted features2017.1129.7811.8926.97——
Rush et al.2017.0429.7611.8826.96——
ABS+Decoding Strategy=beam2017.0429.7611.8826.96——
ABSDecoding Strategy=beam2017.0429.5511.3226.42——
ABSEncoder=Attentive CNN, Decoder=NNLM2017.1129.5511.3226.42——
Yang et al. (truncate)Setting=Supervised, Model Category=NAR, Len (Avg chars)=35.372022.0528.856.4527—-14.75
MOSES+2015.0928.7712.126.4470.5—
CITESTitleSupervision level=Zero-shot, Pre-training=Scientific domain + Paper titles2022.0527.8710.4324.56——
Zeng et al.2017.0427.8212.7426.01——
NACC (length control)Setting=Unsupervised, Model Category=NAR, Len (Avg chars)=47.03, Time (s)=0.0252022.0527.458.8725.14—5.19
NACC (truncate)Setting=Unsupervised, Model Category=NAR, Len (Avg chars)=47.77, Time (s)=0.0122022.0525.798.9423.75—2.21
TEDSupervision level=Unsupervised2022.0525.588.9422.83——
SEQ³Supervision level=Unsupervised2022.0525.398.2122.68——
Char-constrained searchSetting=Unsupervised, Model Category=Search, Len (Avg chars)=44.05, Time (s)=17.3242022.0525.39.2523.43—1.71
BART-LBSupervision level=Zero-shot2022.0525.148.7222.35——
Schumann et al. (truncate)Setting=Unsupervised, Model Category=Search, Len (Avg chars)=45.45, Time (s)=9.5732022.0524.989.0823.18—0.97
CITESSupervision level=Zero-shot2022.0524.758.4221.84——
Su et al. (truncate)Setting=Unsupervised, Model Category=NAR, Len (Avg chars)=45.24, Time (s)=0.0172022.0524.658.6422.98—0
Qi et al. (truncate)Setting=Unsupervised, Model Category=NAR, Len (Avg chars)=44.54, Time (s)=0.0192022.0524.317.6622.48—-1.82
T5-LBSupervision level=Zero-shot2022.05248.1921.62——
PEGASUSSupervision level=Zero-shot2022.0523.397.5920.2——
PREFIX2015.0923.148.2521.73100—
BARTSupervision level=Zero-shot2022.0522.077.4720.02——
Yang et al. (truncate)Setting=Unsupervised, Model Category=NAR, Len (Avg chars)=49.372022.0521.74.620.13—-9.84
BriefSupervision level=Unsupervised2022.0521.265.618.89——
Lead-50 charsSetting=Unsupervised, Model Category=Baseline, Len (Avg chars)=49.032022.0520.667.0819.3—-9.23
COMPRESS2015.0919.635.1318.28100—
IR2015.0916.915.5515.5829.2—
T5Supervision level=Zero-shot2022.0515.674.8614.38——
REFERENCE2015.09———45.6—