Question: Is the baseline a non-heirarchical model like BERT?

Evidence:
raw_evidence: FLOAT SELECTED: Table 1: Results of various models on the CNNDM test set using full-length F1 ROUGE-1 (R-1), ROUGE-2 (R2), and ROUGE-L (R-L).
Our main results on the CNNDM dataset are shown in Table 1 , with abstractive models in the top block and extractive models in the bottom block. Pointer+Coverage BIBREF9 , Abstract-ML+RL BIBREF10 and DCA BIBREF42 are all sequence to sequence learning based models with copy and coverage modeling, reinforcement learning and deep communicating agents extensions. SentRewrite BIBREF26 and InconsisLoss BIBREF25 all try to decompose the word by word summary generation into sentence selection from document and “sentence” level summarization (or compression). Bottom-Up BIBREF27 generates summaries by combines a word prediction model with the decoder attention model. The extractive models are usually based on hierarchical encoders (SummaRuNNer; BIBREF3 and NeuSum; BIBREF11 ). They have been extended with reinforcement learning (Refresh; BIBREF4 and BanditSum; BIBREF20 ), Maximal Marginal Relevance (NeuSum-MMR; BIBREF21 ), latent variable modeling (LatentSum; BIBREF5 ) and syntactic compression (JECS; BIBREF38 ). Lead3 is a baseline which simply selects the first three sentences. Our model $\text{\sc Hibert}_S$ (in-domain), which only use one pre-training stage on the in-domain CNNDM training set, outperforms all of them and differences between them are all significant with a 0.95 confidence interval (estimated with the ROUGE script). Note that pre-training $\text{\sc Hibert}_S$ (in-domain) is very fast and it only takes around 30 minutes for one epoch on the CNNDM training set. Our models with two pre-training stages ( $\text{\sc Hibert}_S$ ) or larger size ( $\text{\sc Hibert}_M$ ) perform even better and $\text{\sc Hibert}_M$ outperforms BERT by 0.5 ROUGE. We also implemented two baselines. One is the hierarchical transformer summarization model (HeriTransfomer; described in "Extractive Summarization" ) without pre-training. Note the setting for HeriTransfomer is ( $L=4$ , $H=300$ and $A=4$ ) . We can see that the pre-training (details in Section "Pre-training" ) leads to a +1.25 ROUGE improvement. Another baseline is based on a pre-trained BERT BIBREF0 and finetuned on the CNNDM dataset. We used the $\text{BERT}_{\text{base}}$ model because our 16G RAM V100 GPU cannot fit $\text{BERT}_{\text{large}}$ for the summarization task even with batch size of 1. The positional embedding of BERT supports input length up to 512 words, we therefore split documents with more than 10 sentences into multiple blocks (each block with 10 sentences). We feed each block (the BOS and EOS tokens of each sentence are replaced with [CLS] and [SEP] tokens) into BERT and use the representation at [CLS] token to classify each sentence. Our model $\text{\sc Hibert}_S$1 outperforms BERT by 0.4 to 0.5 ROUGE despite with only half the number of model parameters ( $\text{\sc Hibert}_S$2 54.6M v.s. BERT 110M). Results on the NYT50 dataset show the similar trends (see Table 2 ). EXTRACTION is a extractive model based hierarchical LSTM and we use the numbers reported by xu:2019:arxiv. The improvement of $\text{\sc Hibert}_S$3 over the baseline without pre-training (HeriTransformer) becomes 2.0 ROUGE. $\text{\sc Hibert}_S$4 (in-domain), $\text{\sc Hibert}_S$5 (in-domain), $\text{\sc Hibert}_S$6 and $\text{\sc Hibert}_S$7 all outperform BERT significantly according to the ROUGE script.

Answers:
There were hierarchical and non-hierarchical baselines; BERT was one of those baselines