Title: TED: A PRETRAINED UNSUPERVISED SUMMARIZA-TION MODEL WITH THEME MODELING AND DENOIS-ING Anonymous

Abstract: Text summarization aims to extract essential information from a piece of text and transform it into a concise version. Existing unsupervised abstractive summarization models use recurrent neural networks framework and ignore abundant unlabeled corpora resources. In order to address these issues, we propose TED, a transformer-based unsupervised summarization system with pretraining on largescale data. We first leverage the lead bias in news articles to pretrain the model on large-scale corpora. Then, we finetune TED on target domains through theme modeling and a denoising autoencoder to enhance the quality of summaries. Notably, TED outperforms all unsupervised abstractive baselines on NYT, CNN/DM and English Gigaword datasets with various document styles. Further analysis shows that the summaries generated by TED are abstractive and containing even higher proportions of novel tokens than those from supervised models.

1. INTRODUCTION:

Summarization refers to the task of condensing a document into a shorter version. Summarization models can be categorized into two classes: abstractive and extractive. Extractive models select sentences from the input article as the summary. Such process ensures a basic level of grammaticality and accuracy, but also limit the model to copying. In contrast, abstractive models summarize documents using tokens and phrases that may not be found in the input article, a process requiring an advanced ability to refine, paraphrase and re-organize information (See et al., 2017; Narayan et al., 2018) .

Like most machine learning algorithms, summarization models can also be divided into supervised and unsupervised categories. Supervised approaches require in-domain parallel data, i.e. both input articles and corresponding reference summaries must be present for training (Hermann et al., 2015; Liu & Lapata, 2019) . Unfortunately, high-quality paired data are not always available across different text domains and styles. Moreover, considering the fact that summarization is not an easy task even for people, reliable human-labeled data are also difficult to obtain. Therefore, several unsupervised summarization approaches have been proposed, which do not require reference summaries for the target domain. We introduce these methods as follows.

Unsupervised extractive models. TextRank (Mihalcea & Tarau, 2004) encodes sentences in the article as nodes in an undirected graph. The weights of edges are measured by sentences similarity. The centrality of a node (sentence) is computed by PageRank (Brin & Page, 1998) to decide whether a sentence should be included in the final summary. Zheng & Lapata (2019) advances upon TextRank by using BERT (Devlin et al., 2018) to compute sentence similarity and build graphs with directed edges decided by the relative positions of sentences.

Unsupervised abstractive models. Baziotis et al. (2019) leverages differentiable sampling and optimizes by re-constructing the input article from the generated summary. Chu & Liu (2018) proposes a similar idea in the multi-document summarization setting. Wang & Lee (2018) uses adversarial training and reinforcement learning to make the summary human-readable. Févry & Phang (2018) adopts denoising autoencoders originally used in sentence compression. However, most of these models are only tested on datasets with considerably small article/summary length. Also, previous models usually utilize the recurrent neural networks (RNNs). However, transformers (Vaswani et al., 2017; Devlin et al., 2018) have shown superior performances over RNNs on various NLP tasks, including machine translation, reading comprehension, sentiment analysis, etc.

In this paper, we present TED, an unsupervised abstractive summarization model with theme modeling and denoising that uses a transformer-based encoder-decoder structure and the pretraining leverages large scale unlabeled corpora. Our main contributions are two-fold as follows.

First, we leverage the lead bias in news articles for model pretraining. The lead bias is introduced by the journalistic convention of writing using an inverted pyramid structure, placing the most important information in the beginning of an article. We propose to use the leading sentences as the target summary and train the model to predict it during pretraining. In this way, we can utilize large-scale unlabeled corpora. Without any finetuing, the model pretrained in this way on 21.4M news articles can yield better performance than most existing unsupervised methods.

Second, to finetune on specific datasets, TED is further trained with a theme modeling loss and a denoising autoencoder. The role of the theme modeling module is to make the generated summary semantically close to the article. The module uses a semantic classifier trained using a discriminative objective function. Furthermore, to optimize on the generated summary tokens, we adopt the Gumbel-Softmax (Jang et al., 2016) estimator to replace the non-differentiable arg max. The denoising autoencoder has been previously used in unsupervised machine translation (Lample et al., 2017) and sentence compression (Févry & Phang, 2018) , and we employ it to help the model extract salient information from corrupted text. Also, instead of classical word tokenization, we adopt the SentencePiece tokenization (Kudo & Richardson, 2018) to alleviates the long-standing out-of-vocabulary (OOV) problem in language generation tasks (Luong et al., 2014; Sennrich et al., 2015) .

We test TED on several benchmark datasets. The experimental results show that TED outperform all unsupervised abstractive baselines on all datasets. For example, on the CNN/DM dataset, it outperforms the state-of-the-art unsupervised abstractive model by more than 9 ROUGE-1 points and compares favorably with most unsupervised extractive models. We further show that TED is capable of generating novel words and phrases in the summaries, and is a highly abstractive system even compared with supervised systems.

2. METHODOLOGY:

In this section, we will go through the model structure of TED, i.e. the transformer encoder and decoder, theme modelling and the denoising autoencoder. The overall architecture of TED is illustrated in Fig. 1 .

2.1. TRANSFORMER ENCODER AND DECODER:

Previous unsupervised summarization methods are based on the sequence to sequence (seq2seq) model (Sutskever et al., 2014) that primarily uses the RNN model. As the transformer structure (Vaswani et al., 2017) has been successfully used in a large number of NLP tasks, our model employs the multi-layer transformer encoder-decoder architecture. We follow the standard transformer design in our network and refer readers to Vaswani et al. (2017) for more technical details. Denote the number of layers (i.e., Transformer blocks) as L, the number of self-attention heads as H and the hidden size as N . We explore two different configurations in experiments, 4 layers 4 heads (4L4H) with N = 512 and 10 layers 8 heads (10L8H) with N = 720.

Denote the input article token sequence as X = {x 1 , x 2 , ..., x n }, and each token is transferred to a vector by a trainable embeddings matrix V . The output from transformer encoder

E is a sequence of encoded vectors E(X) = {u E 1 , u E 2 , ..., u E n }.

The decoder can be viewed as a conditional language model to generate the summary. Given k input summary tokens W = {w 1 , w 2 , ..., w k }, the cross attention layer in the decoder D attends with encoder outputs {u E i } n i=1 . The decoder outputs are

D({w 1 , w 2 , ..., w k }) = {u D 1 , u D 2 , ..., u D k }.

The probability distribution over the vocabulary for w k+1 is given by:

EQUATION

In our model, the text are not tokenized by spaces but by the SentencePiece (Kudo & Richardson, 2018) model, in order to address the challenging out-of-vocabulary (OOV) words issue. Efforts have been made to address this issue at the cost of losing semantic information, such as mapping OOV words to a special "UNK" token.

To mitigate the open vocabulary problem, we adopt SentencePiece, a data-driven method that trains tokenization models from sentences in large-scale corpora. The advantage of the SentencePiece model is that its subwords can cover all possible word forms and the subword vocabulary size is controllable. In our experiments, we train a SentencePiece subword vocabulary of size 32,000.

Note for supervised summarization models, the inputs to the decoder are the groundtruths/reference summary tokens; for unsupervised learning, input tokens are generated in the previous pass. More details are available in section 2.3.1.

2.2. PRETRAINING WITH UNLABELED CORPORA:

Leveraging large scale unlabeled text corpora to pretrain models has been proven as an effective method in multiple NLP tasks (Devlin et al., 2018) . However, such approach has not yet been utilized in text summarization.

News articles follow an inverted pyramid structure, i.e. front loading the most salient information. This so-called "lead bias" for news summarization is so strong that See et al. ( 2017) have shown that using the first 3 sentences in a news article as a summary can score higher than many sophisticated deep learning models. Although this poses a great challenge to previous research, we leverage this property in our favor in the pretraining phase.

For a news article, we set the target summary to be the first three sentences. This allows the model to exploit the structural bias of the news domain and infer the most important information using the background materials in the remainder of the article. For pretraining, we obtain three years of online news articles from 2016 to 2019 via an industrial search engine. The search engine indexes major online news domain, for instance, New York Times and Bloomberg. Then we collect the parsed articles within the 2016-2019 time range as the raw data. Note that this time span does not overlap any of three test datasets we use in this paper, therefore the pretraining should not lead to data leakage in test.

Next we conduct data cleaning to remove irrelevant distracting content and filter out articles whose top three sentences do not form a good summary. First, many news articles begin with media names, reporter names, dates or other irrelevant information for summarization, e.g. "New York (CNN) -", "Adam Smith, June 3rd 2018:". We automatically clean these using regular expressions. Second, we only include articles whose top three sentences contain between 10 and 150 words, and remaining sentences contain between 150 and 1,200 words. Third, we try to remove articles for which the first three sentences may not contain the major information in the article. We use a simple and easy-tocompute metric: overlapping words. We compute the portion of non-stopping words in the top three sentences that also appear in the rest of an article. A higher ratio indicates that the rest of the article is likely to elaborate on the beginning part. We keep those articles with this ratio of overlapping words higher than 0.65.

Finally, we end up with 21.4M articles. We randomly sample 12,000 from the data for validation. We conduct pretraining for 10 epochs and pick the model with the best ROUGE-L score on the validation set. The pretraining idea is also used in (Anonymous, 2020) . After pretraining, we finetune TED on target datasets in an unsupervised manner. This includes two modules: theme modeling and denoising autoencoder.

2.3. THEME MODELING:

Theme modeling aims to make the generated summary semantically close to the input article. We employ differential sampling to enable optimization on generated summaries and train a classifier to improve the semantic relatedness between the summary and article. 

2.3.1. DIFFERENTIABLE SAMPLING:

In order to optimize the network on output summaries, we need to make the generation of summary tokens differentiable. Recall the probability distribution of token w k+1 is P (w

k+1 |w 1:k , x 1:n ) = softmax(V u D k ). Let π denote P (w k+1 |w 1:k , x 1:n ).

One can use arg max on π to obtain the token w k+1 in the forward pass, however, it is not differentiable in the gradient back-propagation. Although one can get around by obtaining the embedding of w k+1 as a weighted sum of the vocabulary embeddings V , this results in an undesirable gap between the forward pass in training (weighted sum) and inference (discrete sampling). To solve this issue, we employ the straight-through Gumbel-Softmax estimator (Jang et al., 2016) as in Yang et al. (2018) ; Baziotis et al. (2019) . Specifically, the forward pass in training still uses arg max sampling, but for gradient computation, the following Gumbel-Softmax distribution is used as a differentiable approximation for the arg max operation:

EQUATION

where g 1 , • • • , g k are i.i.d samples drawn from the Gumbel distribution G(0, 1) and τ denotes the softmax temperature. As shown in Jang et al. (2016) , as τ → 0, the Gumbel-Softmax distribution converges to the categorical (one-hot) distribution; as τ → inf, the Gumbel-Softmax distribution converges to the uniform distribution. Although this gradient estimator is biased, we find that this method works well in practice. We choose τ = 0.1 based on the CNN/DM validation set and use this value in all the experiments. Denote the input article as d, the generated summary as s = {w 1 , w 2 , ..., w m }. The generation of s follows the recursive process that input w 1:k to the transformer decoder to obtain w k+1 , then input w 1:k+1 to compute w k+2 and so on. The first input token w 1 is always the special beginning token [START].

2.3.2. ENCODER TRANSFORMER AS A SEMANTIC CLASSIFIER:

We frame the semantic similarity problem in a discriminative setting. As the generated summary may be off the article theme at the beginning of training, we add sentence pairs from the article to facilitate similarity computation.

Concretely, during training, we pick two consecutive sequences of tokens a 1 and a 2 from an article to form a positive sequence pair {a 1 , a 2 }. Second, sequence b 1 is chosen from a random article from the dataset to form the negative sequence pair {a 1 , b 1 }. Following Devlin et al. (2018) , each sequence pair is packed into one single sequence by inserting a special token [SEP] between them and adding trainable segment embeddings. A special classification token [CLS] is also added to the beginning. As shown in Fig. 2 , the packed sequence is fed as input into TED's encoder. The output vector associated with the token [CLS], is then classified into similar/distinct categories by a two-layer fully connected network. We use the following cross-entropy loss to optimize the encoder. Note that the theme modeling loss does not involve the transformer decoder. Figure 2 : Theme modeling is essentially a semantic classifier. The input sentence pair is first processed by adding a "class" token in the beginning and a "separation" token in between. Then the sentence pair is fed into the transformer encoder, and then a linear classifier.

L theme = -log(p(y = 1|a 1 , a 2 )) -log(p(y = 1|s, d)) -log(p(y = 0|a 1 , b 1 )) (3)

2.4. DENOISING AUTOENCODER:

The idea of denoising autoencoder (Vincent et al., 2008) has been used in unsupervised machine translation (Artetxe et al., 2017; Lample et al., 2017) to prevent the model learning to merely copy every input word one by one. This denoising process imitates text simplification and helps to refine essential semantic information.

In detail, a sequence of n consecutive tokens x from the input article is injected with two types of noise. First, we insert noisy tokens sampled from other articles in the same dataset into the original sequence at random positions, obtaining a new sequence with length n , where n is 40%-50% larger than n. Next, similar to Lample et al. (2017) , the sequence is slightly shuffled by applying a permutation σ such that ∀i ∈ [1, 2,• • • , n ], |σ(i) -i| ≤ k, where the permutation distance k is set to be 20% of the length of x. The final corrupted sequence is denoted as x .

The TED model is trained to recover the original token sequence given the corrupted sequence:

EQUATION

where CE denotes the mean of token-level cross-entropy loss. TED(x ) denotes the sequence of probability distribution outputs {π} from the decoder with inputing x to the encoder and x to the decoder for teacher forcing.

The final objective function is the mean of Eq. (3) and Eq. ( 4):

EQUATION

3 EXPERIMENTS

3.1. IMPLEMENTATION DETAILS:

For pretraining, we use a dropout rate of 0.3 for all inputs to transformer layers. We use RAdam (Liu et al., 2019) as the optimizer, with a learning rate of 10 -4 . Also, due to the different numerical scales of the positional embedding and initialized sentence piece embeddings, we divide the positional embedding by 100 before feeding it into the transformer.

For unsupervised finetuning on specific datasets, the learning rate is set to 2 × 10 -4 and dropout ratio stays the same as in pretraining. 

3.2. RESULTS:

We evaluate our model on three summarization datasets: NYT, CNN/DM and English Gigaword.

The text statistics on these datasets are summarized in Table 1 . Numbers of NYT and CNN/DM are collected from Zheng & Lapata (2019) .

We compare TED with the following baselines: Brief (Wang & Lee, 2018) , SEQ 3 (Baziotis et al., 2019) , GPT-2 (Radford et al., 2019) , TextRank (Mihalcea & Tarau, 2004) , PACSUM (Zheng & Lapata, 2019) , PGNet (See et al., 2017) and REFRESH (Narayan et al., 2018) . These models cover both unsupervised and supervised categories, and include abstractive and extractive methods.

We measure the quality of generated summaries by ROUGE F1 score (Lin, 2004) , including unigram (ROUGE-1), bigram (ROUGE-2) and longest common subsequence (ROUGE-L).

The NYT dataset (Durrett et al., 2016) (See et al., 2017) . Considering this factor, TED achieves results that are competitive with unsupervised extractive baselines and surpasses all unsupervised abstractive models.

The CNN/DM dataset (Hermann et al., 2015) is composed of articles from CNN and Daily Mail, and uses associated highlights as reference summaries. We use the same training, validation and test split (287,227/13,368/11,490) as other baselines. Similar to See et al. (2017) and Liu & Lapata (2019) , input articles are truncated to 500 tokens. Results are shown in Table 2 . TED with a larger model size (10L8H) outperforms all unsupervised abstractive methods and compares favorably with unsupervised extractive baselines. Note that TED outperforms GTP-2, a powerful transformer-based language generation model pretrained on large scale webpage textual data, by siginificant margins. Again, TED further improves upon pretrained models on both 10L8H and 4L4H configurations.

For the English Gigaword sentence compression dataset, the input text is the first sentence of a news article, and the reference summary is the article's headline. The size of train/val/test is 3.8M/189k/1,937 respectively, after filtering out data examples with articles containing only "UNK" tokens. As shown in Table 3 . TED outperforms all the unsupervised baselines.

4. DISCUSSION:



4.1. ABLATION STUDY:

The ablation studies shown in 

4.2. MODEL ANALYSIS:

Novel N-grams. To examine how abstractive TED is, we compute the proportion of novel N-grams in the summary output (Fig. 3 ). The reference summary and the output from PGNet are included for comparison. Although TED is unsupervised, it includes more novel grams than the supervised model PGNet. The reference summaries have the highest proportion of n-grams.

Example. We showcase a sample summary from CNN/DM dataset along with the input article and the reference summary (Fig. 4 ). As shown, TED is able to capture and organize the essential information into fluent language. We attribute the grammatical correctness to the pretraining process and the denoising autoencoder. However, we also note that although TED manages to recognize the temporal information related to reported event (a few hours after Fox news reports), it makes a mistake by summarizing as "a few hours after a report about roberts' research was released. . . ". It shows that fact cross-checking is a potential future research direction.

Article after exposing potential security risks with airlines' in-flight entertainment systems, one of the top experts on counter-threat intelligence in the world was pulled off a flight by fbi agents. chris roberts, who featured in a string of fox news reports, was yanked off his plane after it landed in syracuse, new york, on wednesday night by two fbi agents and two uniformed officers. roberts, who works for security intelligence company one world labs, was questioned for the next four hours ...

TED Summary:

chris roberts, who works for security intelligence company one world labs, was pulled off a plane in syracuse, new york, on wednesday night by two fbi agents and two uniformed officers. the incident occurred only a few hours after a report about roberts' research was released by the government accountability office earlier this week.

Reference chris roberts of one world labs grabbed after plane landed in syracuse. two fbi agents spent four hours questioning him about cyberhacking. agents confiscated electronic devices and computer files from roberts. he flew in to give talk at aerospace conference about plane vulnerabilities. roberts featured on fox news' on the record with greta van susteren. regarded as one of the world's top experts on counter-threat intelligence." Figure 4 : An example of a generated summary by TED. The reference summary and parts of the input article are also included.

5. CONCLUSION:

In this paper, we propose TED, an unsupervised abstractive text summarization model. First, we introduce an effective and powerful pretraining approach leveraging the lead bias in news articles.

We then develop a finetuning scheme to induce the semantic similarity between summaries and input articles, together with a denoising autoencoder. Experiments across three datasets show that TED outperforms unsupervised abstractive baselines. For future work, we would like to encode the criteria of relevance, informativeness and importance proposed in Peyrard (2019) into TED. Fact cross checking is another interesting direction as mentioned in the section 4.2.

:



Figures and Tables:

Figure 1: Overall structure of our model. TED first pretrains on news articles and then finetunes with theme modeling and denoising. (from left to right).

Figure 3: Proportion of novel grams in summaries on the CNN/DM test set. We compare three systems, TED, PGNet and reference summaries. Numbers of PGNet are computed from its publicly released output.

The batch size is 16, and the vocabulary embeddings are also updated in the training process. During test, we generate the summarization from trained encoder and decoder by beam search. Average document and summary length in number of words and sentences on NYT, CNN/DM, and English Gigaword datasets (test set).

Table content: <table><tr><td>Dataset</td><td># docs</td><td colspan="4">avg. document avg. summary words sen. words sen.</td></tr><tr><td>CNN/DM</td><td>11,490</td><td>641.9</td><td>28.0</td><td>54.6</td><td>3.9</td></tr><tr><td>NYT</td><td colspan="3">4,375 1,290.5 50.7</td><td>79.8</td><td>3.5</td></tr><tr><td colspan="2">English Gigaword 1,937</td><td>29</td><td>1</td><td>8</td><td>1</td></tr></table>

contains 110,540 news articles with 100,834/9,706 train/test split. FollowingLiu & Lapata (2019), we choose 4,000 examples as the validation set and filter out examples with summaries of fewer than 50 words. As demonstrated by the results in table 2, the unsupervised fine-tuning of TED improves upon the pretrained model by 2.75%/1.06%/2.37% on ROUGE-1/ROUGE-2/ROUGE-L respectively. Note that ROUGE metric prefers extractive systems that preserve original phrasing

Table content: <table/>

Peer review (iclr2020):
Paper*s Claims The paper introduces a new unsupervised abstractive summarization approach called TED, using a Transformer encoder and decoder. Their main contributions are as follows: 1) Pretraining the encoder and decoder on news articles using the first beginning as the target summary. 2) Fine-tune on other datasets using so-called theme modeling, and separately a denoising loss. 3) TED*s performance is claimed to significantly improve over GPT-2 while not being too far from the best unsupervised extractive summarization results. Decision Edit: After revisions and discussions, I recommend we accept this paper. I am leaning towards accepting this paper mostly because of the contribution #1 above. Unsupervised learning using large quantities of text that have the property of being typically written in a style that synthesizes information in the first 1-3 sentences is a powerful idea. That the performance is improved compared to other unsupervised abstractive summarization confirms the importance of this approach. However the importance of and justification for the fine-tuning steps are comparatively much more limited in my opinion. Also, some important details about the preprocessing for pre-training appear to be missing and they could be quite important. Detailed arguments for decision I view this effort as aiming to reproduce the BERT approach in the context of abstractive summarization, which is a good idea. The most clever contribution is in leveraging un-labeled text using the first few sentences as the target summary for pretraining. The results of just this part are already beating previous approaches, while not requiring any in-domain data, which is quite powerful. However, some relatively important details regarding the methodology are omitted or only glossed over and it would greatly contribute to making this work more reproducible if the details were included (see my detailed notes below, notably regarding section 2.2). On the fine-tuning steps, I have several worries. First, why not fine-tune using supervised learning, as would be the analog to the BERT approach? Instead the authors go out of their way to do in-domain unsupervised learning, which provides a boost, yes, but still doesn*t compare positively to extractive and/or supervised methods. Second, why not perform the theme modeling and denoising also -- or rather only -- on the unlabelled pretraining data? Why should it be done on the in-domain fine-tuning data instead (while not using the most valuable piece of in-domain information, namely the example summaries)? After all, it*s a fully unsupervised approach and it can actually be performed on any text at all, whether a summary for it exists or not. Again regarding the unsupervised approach, and to push the BERT analogy further, I*m wondering why not initialize the pretraining model with a BERT-style trained model? After all we could imagine building a system that adds more and more in-domain characteristics sequentially: first pretrain a BERT model, then fine-tune to summarization using what this paper calls pretraining, and then finally fine-tune again to a specific summarization domain. So, to conclude, I find that this paper goes in the right direction and introduces important ideas for pretraining and fine tuning unsupervised abstractive summarization models, but that some decisions about how to use the various ideas (theme and denoising but no supervised learning, in-domain vs during pretraining) have not been explored enough. Extra notes page 2, second line: pretrainleverages (typo) section 2.1: fix first sentence to make it an actual sentence. section 2.2: *we obtain three years of online new articles ... via a search engine* please be more specific about your methodology. section 2.2: You should double check more throughly that there is no data leakage in test. There could be articles about the same exact events, years apart, for example. I doubt that this would be a big effect, but there are easily ways to find highly similar articles between the pretraining data and test data to make sure. section 2.2: *Next we conduct following data cleaning* fix (typo?). Also that sentence probably belongs to the next paragraph. section 2.2: Why did you pick the values that you did for the preprocessing heuristics (such as between 10-150 words, 150-1200 words, 3 sentences and not 2 or 1 or 4, the ratio 0.65, etc.)? Were other values tried? section 2.2: You mention you end up with 21.4M articles. How many were there to start with? What*s the filtering ratio? section 2.2: You mention that you pick the model with the best ROUGE-L score on the validation set. How many models were there? What was different between them? section 2.2, OOV Problem: the information in this whole subsection would fit better in 2.1 where *tokens* are left generic without specifying which type of token you*re considering. Figure 1: I find the upper part of this figure very confusing. Why are there arrows going from the encoder/decoder to a summary, to theme loss, to article and back to encoder/decoder? It*s important that the summary is never seen by the theme loss otherwise it*s not unsupervised anymore, and I also don*t see why the arrow would go through article *after* theme loss. I assume there must have been a mistake, please fix. section 2.4: *the sequence is slightly shuffled by applying a permutation /sigma such that ...* The formula given here tells me that all token indices are shuffled with another token within a window k. That seems like a lot of moving around, and also depending on the implementation a token from the beginning could possibly end up at the very tail of the sentence by being picked iteratively again and again, thus falling outside the permutation distance k. Please provide more details on how this is done and a justification for why it was decided to do it this way. Section 3.1: I*d like to know how long (preferably number of words, or at least number of wordpiece tokens) the summaries generated are. What determines how long they are, is it a fixed size, or the model decides to stop on his own (or when hitting some limit), or something else? section 4.2: Do you have any idea why your unsupervised approach yields more novel n-grams than a the supervised model you compare against? This can be good as much as it can be bad, in that it could be going off-track. Yes humans have high novelty, but high novelty in itself isn*t necessarily good. I don*t find the argument that have more novel ngrams is intrinsically, necessarily good, compelling. If I*m wrong, then it would be nice to have better explanation in the paper.