Improving the Robustness of Summarization Models by Detecting and Removing Input Noise
Authors: Kundan Krishna
Yao Zhao
Jie Ren
Balaji Lakshminarayanan
Jiaming Luo
Mohammad Saleh
Peter J Liu
Venue: EMNLP 2023 Findings (2023)
Keywords: summarization
robustness to noise
safety in ML

Abstract: The evaluation of abstractive summarization models typically uses test data that is identically distributed as training data. In real-world practice, documents to be summarized may contain input noise caused by text extraction artifacts or data pipeline bugs. The robustness of model performance under distribution shift caused by such noise is relatively under studied. We present a large empirical study quantifying the sometimes severe loss in performance – up to 12 ROUGE-1 points – from different types of input noise for a range of datasets and model sizes. We then propose a light-weight method for detecting and removing such noise in the input during model inference without requiring any extra training, auxiliary models, or even prior knowledge of the type of noise. Our proposed approach effectively mitigates the loss in performance, recovering a large fraction of the performance drop, sometimes as large as 11 ROUGE-1 points.

Improving the Robustness of Summarization Models by Detecting and Removing Input Noise
Kundan Krishna
Yao Zhao² Jie Ren² Balaji Lakshninarayanan"
Jiaming Luo² Mohammad Saleh² Peter Liu
Carnegiie Mellon University, work done while at Google Research GGoogle Research
"Correspondence to: kundank@andrew. cmu. peterjliu@google.co
Abstract
the form of OCR errors (Jing al., 2003). How- ever, the impact of different kinds of noise on mod- ern abstractive summarization systems, and ways to accurately detect and remove that noise, remain largely unknown.
The evaluation abstractive summarization models typically uses test data that is identi- cally distributed as training data, In worldworld practice, documents to be summarized may contain input noise caused by text extraction artifacts or data pipeline bugs. The robust- ness of model performance under distribution shift caused by such noise is relatively under- studied. We present a large empirical study quantifying the sometimes severe loss in per- formance to 12 ROUGE-I points from different types of input noise for range of datasets and model sizes. We then propose light-weight method for detecting and remov- ing such noise the input during model infer- ence without requiring any extra training, aux- iliary models, or even prior knowledge type of noise. Our proposed approach effec- tively mitigates the loss in performance, recov- ering a large fraction of performance drop, sometimes large 11 ROUGE-1 points.
In this work, we study how noise in the input af- feets the output generated by summarization mod- els, and propose a method to detect and remove it. We synthetically inject 4 types of noise to 4 ab- stractive summarization datasets with diverse styles (Narayan al., 2018; Kim et al., 2019; Gliwa al., 2019; See al., 2017), and quantify the drop in aggregate metrics for the output summaries (Sec- tion 3). We also study how the quality of generated summaries varies with factors such as the amount of noise and size of the models. For our exper- iments, we use PEGASUS (Zhang et al., 2020) models - transformer- based pre-trained models which deliver competitive performance across ab- stractive summarization benchmarks.
We present a method to detect and remove noisy spans in the input, which works without prior knowledge of the noise type access to its sam- ples, yet can recover a large fraction of the drop in output quality resulting from noise addition (Sec- tion 4). Our approach for detecting noisy spans is based on variations of the Relative Mahalanobis Distance OOD Score proposed by Ren et al. (2023), which uses the embeddings computed by the sum- marization model's 's encoder. Our approach does not require any additional training use of exter- nal models, hence it is relatively efficient. Figure shows method's impact sample noisy doc- ument.
1 Introduction
Despite rapid progress in abstractive summariza- tion in recent years (Lewis et al., 2020; Raffel et al., 2020b; Zhang et al., 2020), virtually all works have tested models using test data which is identically distributed the training data, and little attention has gone into studying their robustness to input distribution shift caused by input noise. Data from different domains which have been addressed in summarization research, may contain noise of dif- ferent types. For example, when summarizing a news article on a web page, there can be embedded elements such as ads or tweets which may be in- cluded part of the article due to erroncous text extraction. A system summarizing chatroom con- versations might encounter artifacts such as URLs, or sometimes even code shared between partici- pants. If the text to be summarized is acquired by scanning a document, noise can be introduced in
Finally, we investigate how different parts of the model architecture cause the drop in output quality upon adding noise the input (Section 5). We attribute the performance drop to two phenomena: (i) corruption of the representations of non-noisy input tokens computed by the encoder due to con- textualization with neighboring noise; and (ii) dis-

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Document:
The Office for National Statistics said industrial output fell 0. compared with January, when it dropped 0.3% http://southafric.ccm co za/robberr-ssland Unexpectedly warm weather drove the change, because it led to a fall electricity and gas demand, ONS ittes h/sch//aaa//orrs...... uvm.edu Construction output fell 1.7% February, down from revised January reading of zero growth. The construction figure, the biggest drop in nearly year, was meinly the result of 2.6 fall In the
housebuilding sector. http::/wwww.tt travelleeiggncruiisss.s com/terms conditions/ Meanwhile, the UK's deficit goods and services widened to 23.7bn in February, from a revised figure of E3bn January. hitps://work.cn www.k.chro..coo cttpon/ncm///eeeeeeeeeeeeeeeeeee
com au/ According to the ONS, the deficit was fuelled by what called 'erratic items', such imports of gold and aircreft. "The overall trade deficit worsened, excluding erratic items, the picture improved, as imports fell more than exports' said ONS senior statistician Kate Davies. Howard Archer, chief UK and European economist at IHS Markit, called figures disappointing package of data for the economy which fuels suspicion that GDP growth slowed markedly, largely due consumers becoming more cautious", experiment html, He added We suspect UK CDP growth the first quarter of 2017 slowed to 0 0.4% quarter on quarter from 0.7% quarter 2016 would
/san//jaeejjeiniieee wtweeeino/////////////e/ee/////e en1/217706/asch
quarter-on-que quuattr----quarteriie in
weakest growth since
quarter 2016,
Summary before noise addition: 'Activity in the UK's industrial and construction sectors slowed in February, according to official figures." Summary after noise addition: "Here are some the highlights from the latest economic data for the UK!'
Summary after noise filtering: "Activity the UK's industrial and construction sectors shrank February, according official figures." Ground truth summary: 'Activity in the UK's industrial and construction sectors shrank in February, new figures show.'
Figure 1: Effect of noise addition and filtering the model generated summary for sample document. Random URLs are injected to the original document as noise. The color indicates the value of our proposed OOD score for a text span - red represents positive and blue represents negative OOD scores, with saturation proportional to the magnitude. Removing the detected noisy parts from input and feeding to summarization model results in summary closer to the ground truth.
traction of the decoder such that it assigns non-zero attention to the representations of noisy input to- kens. We perform an ablation where we remove the encoder embeddings of the noisy tokens before running the decoder, hence eliminating the effect of decoder distraction. We find that in majority of cases this leads to partial recovery in output quality suggesting that generally both factors are responsi- ble to some extent for the poor output summaries. We make the following contributions:
the effect of noise from ASR errors CNN based summarization models. In contrast, we experiment with pre-trained Transformer models which are now preferred popular use due to their superior performance (Lewis al., 2020; Zhang et al., 2020; Raffel et al., 2020b), and address a wide variety of noise types and summarization datasets. Contem- porary to our work, Chen et al. (2023) have studied the impact of misspellings in input to summariza- tion models, whereas our work instead focuses additive input noise and its explicit removal.
We quantify the impact of various kinds of noise on pretrained Transformer- summa- rization models, demonstrating drops in output quality up to 12 ROUGE-1 points.
The effect of noisy inputs has also been studied for NLP tasks other than summarization, such as machine translation (Niu et al., 2020) and question answering (Peskov al., 2019). Multiple works across machine translation (Karpukhin et al., 2019; Vaibhav et al., 2019), question answering (Peskov et al., 2019) and summarization (Jing et al., 2003) have used synthetic noise to create noisy inputs. Similar to these works, we also create synthetic noisy inputs due to lack of a dataset with naturally occurring labeled noise. One distinguishing aspect of our work is that our noise detection/remooal method works without exposing the model the noise during training, which is closer to practical scenarios where unknown types of noise can be encountered after a model is deployed.
We show that this noise can be detected using adaptations of an --ditiiiiiiiinoo detection technique, without ever being exposed to it in advance. Our approach can recover much of the performance drop (sometimes as large as 11 ROUGE-1 points), improving robustness and safety for real-world model deployment.
We examine how different parts of the model's computation are affected by the introduction of input noise, leading to generation of inferior summaries.
2 Related Work
3 Impact of noise addition
Research on the behavior of summarization models on noisy inputs is quite sparse. Jing et al. (2003) investigated how extractive summarization models are impacted by OCR errors in scanned documents. More recently, Meechan-Maddon (2019) studied
We inject noisy text spans in between sentences of the clean articles. The insert position of each noisy text span is sampled independently and uniformly at random (see Figure in Appendix for an exam-

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ple). Overall, we consider the following choices of a noisy text span:
clear gap between the drops for Code and Ran- domsent vs Emoji and URL, with the gap widening as the noise amount is increased.
Code a random line of code from a corpus of Python programs (Husain et al., 2019). Code may be shared in professional chatrooms.
Effect of noise type: In general, els are more robust URLs and emojis, and less robust Randomsent and Code noise types as demonstrated by performance drops (averaged across model sizes) shown in Figure 2c. We suspect that some of the this could be due to the presence of URLs and emojis in the training dataset itself, due to which the model may have learned to be robust to those noise types. In addition, from Figure 2c we see that models trained on different datasets have varying sensitivity to different kinds of noises, For example, SAMSum notoriously susceptible to Randomsent noise, leading to a drop of about 10 Rouge-I points averaged across model sizes (Ta- ble in Appendix), whereas for CNN/DailyMail Code is the most harmful type of noise.
the mod-
Emoji - randomly sampled emojis taken from the version 15 release on unicode. org. Emo- jis can be found in conversations and social media posts.
URL random URL from the first 1% of val- idation set of the the Colossal Common Crawl Corpus(C4) (Raffel et al., 2020b). URLs can be referenced in news articles or mentioned in chatrooms.
Randomsent a random sentence from the first I 1% of validation set of the C4 corpus.
We experiment with different amounts of noise hyper-
added to the input which is treated parameter. We measure the amount of noise in terms of the number of noisy tokens added to the input divided by the total number of tokens the input after noise addition. We experi- ment with 4 different datasets - XSUM (Narayan et al., 2018), CNN/DailyMail (See et al., 2017), SAMSum (Gliwa et al., 2019) and ReddirTIFU- long (Kim et al., 2018). Our datasets span a vari- cit of domains, where the first two datasets deal with summarizing news articles, and the remaining two consider summarizing conversations and so- cial media posts respectively. For experiments with each summarization dataset, we use PEGA- SUS models (Zhang et al., 2020) finetuned on that dataset. We evaluate the performance of models using ROUGE scores (Lin, 2004) of the correspond- ing summaries generated by the them.
Effect of model size: We compare PEGASUS models of 3 different sizes (number of parameters)
Small (50M), Base (200M), and Large (500M). As shown by performance drops (averaged over noise types) in Figure 2d, one might expect larger models be less susceptible to noise, but it does not seem be the case in general and simply scal- ing up models may not solve robustness. some cases, large models can still suffer loss of over 10 ROUGE-1 1 points with addition of noise (see Ta- ble 6 in Appendix).
A qualitative analysis of the summaries gener- ated for noisy inputs revealed that there exist some frequent bad summaries which are generated by the models for many noisy inputs. This is observed for models fine-tuned on XSUM and RedditTIFU- long datasets, while for the other two datasets we did not observe such pattern. We show five of the most frequently generated summaries for XSUM and ReddicTIFU-long in Table 1. We see that the generated summary (for noisy inputs) is often just punctuation marks such as a period or a semicolon. Notably, for XSUM dataset, some of the frequently generated bad summaries were also present as ground truth summaries in the train set. For example, "All images are copyrighted." was the ground truth summary for 39 articles in the train set. This suggests that upon encountering in- put noise, the model can fall back to behaving like an unconditioned language model and generating high frequency sequences from the train set.
Effect of noise amount: We compare four differ- ent levels of noise, 5%, 10%, 25%, and 50% (50% means the amount of noise tokens equal to the amount of the clean tokens.). As shown in Fig- ure 2, we see a near monotonic decrease in output quality as more noise is added to the data. In Fig- ure 2a, we group it by datasets while averaging across model sizes and noise types. This reveals that some datasets are more robust to noise than oth- ers (e.g. CNN//ailyMail is most robust), and the relative trends in performance drops remain similar across different noise amounts. In Figure 2b, we group the performance drops by noise types while averaging across datasets and model sizes. We see

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Change performence metric
Change performance metric
-2
-3
CaN-Dailymail Reddit-TIFU
emoji url -4
5
Samsum
code
XSum
randomsent
0.1
0.2
0.4
0.5
0.3
Amount of noise
Amount of noise
(a) Effect of noise by dataset
(h) Effect noise amount hy noise type
Change performance metric
Change performance metric
-3
-4
-4
-5
-6
emoji
-6
uri
-7
small
code
base
randomsent
large
XSim
CNN-Dailymail
Samsum
Raddit-TFFU
XSum
CNN-Dnilymail
Samsum
Reddit-TIFU
(c) Effect noise type datasct
(d) Effect of model size by dataset
Figure 2: Change in output quality upon addition of noise to inputs, while varying different factors noise amount in (a) and (b), noise type in (c), and model size in (d). In (c) and (d) also show the quality after noise removal (the shaded area). Quality is measured as the geometric mean of ROUGE-I/2/L. scores and averaged over the non-varying factors. We set noise amount to 0.5 in (c) and (d).
4 Noise detection and quality recovery
from C4 (Raffel et al., 2020a) which represents broad set of domains. In our experiments 10, 000 examples to fit each distribution. The RMD score is regarded that indicates how close the input sequence is to the in-domain compared to the background domains. A negative score suggests relatively in-domain, while positive score suggests OOD.
4.1 Noise detection
Ren et al. (2023) studied various methods for de- tecting OOD inputs for conditional language gen- eration tasks. They showed that the proposed embedding based OOD detection method Relative Mahalanobis distance (RMD) worked well. Specif- ically, given an input sequence I1 ... 21, the method obtains the input embedding z E,,, by averaging the encoder's final-layer hidden state vectors h, corresponding the input sequence to- ken 24. The OOD score is defined as the difference between two Mahalanobis distances (MD),
background contrastive score
Instead of computing a single OOD score for the entire input document sequence as in (Ren et al.. 2023), in this work, focus detecting smaller sub-parts of OOD noise within the input document sequence. We propose three variants:
Leaveout-SSetence (LO-Seet) In this case, we compute the OOD scores of the input with and without a sentence it. The negative of the change in the OOD score after removing the sentence de- notes the OOD score that sentence. Intuitively, if removing the sentence decreases the overall OOD score, that sentence is assigned a positive OOD score and vice-versa.
S(x) R=D(=)) MD,,, MDo(=), (1) where (z) = (z 11) I- - 1u) mea- sures the distance from z to the fitted in-domain Gaussian distribution NV E), and MD,(z) (4) measures the distance to the fitted background Gaussian N ((4o Eg). The in-domain Gaussian distribution is fitted using the in-domain training data, and the background distri- bution is fitted using the same number of examples
(z 1400 - x>p
SLo-sen( S(x11)) - ((31((-1)0+1)))
(2)

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XSUM
ReddicTIFU-ong
Summary
Noisy
Clean
Summary
Noisy
Clean
(period)
145
1
(colon)
230
0
A chronology of key events:
108
0
68
2
All images copyrighted.
62
7
i'm f**king idiot.
16
3
All pictures are copyrighted.
4
i'm an idiot.
15
22
The following is summary of key events:
0
13
0
Table I:
The frequencies of most commonly generated summaries noisy versions of XSUM and RedditTIFU- long validation sets (Noisy) and their frequencies before adding noise (Clean) (using the base model size and Code noise type with noise amount set 0.5)
Token (LO-Tok) This is very similar to the previous method LO-Sent except that instead of removing sentence, we remove token at time and hence get OOD scores for each token,
each token with its ground truth label and we com- pute the ROC AUC scores for comparison. For the two sentence level scores, SLo-Sent (2i:)) and Seett (Fag)) we assign each token's OOD score be the sentence level OOD score for the sentence which contains that token. We compute evaluation metrics two ways: (i) per-example basis where the AUC score is computed for each example and then they are all averaged across the dataset. (ii) overall basis where all the predictions across the entire dataset pooled together before computing single AUC score. We show the scores averaged across the 4 datasets in (Table 2). In general, the LO-Tok method performs the worst of the three OOD-based methods, while Sent and LO-Sent per- form comparably. Comparing the GPT-2 baseline with LO-Tok, GPT-2 performs clearly better for Randomsent, comparably for Code, and clearly worse for Emoji and URL noise types. However, GPT-2 lags behind LO-Sent and Sent for all noise types. Between Sent and LO-Sent, Sent performs better for Code and Randomsent and LO-Sent per- forms better for Emoji and URL noise types. For its simplicity, we use the Sent method for OOD detection in rest of the paper.
SLo.To (a.) = S(x):) - (1 (3)
Sentencewise (Sent) Instead of computing the score based on embeddings averaged over the tokens in the whole input document sequence (consisting of multiple sentences), we fit Gaus- sian distributions at the sentence level by averag- ing the token embeddings in sentence Zi:j I+1+ Ek_, hg- We use the sentence embed- dings from in-domain data and data to fit the two Gaussian distributions, A Muuom Esent) and
N N(ug D0mm).
Ssem (Fag) MD (zzg) MD (Zez)
(4)
where MD.ent and MD are MDs to N N( sent sent and (44 sent = respectively.
GPT-2 likelihood We also experiment with a simple language model baseline to generate the noisiness scores based on average negative log- likelihood (NLL) of tokens in a sentence, as given by the pretrained GPT-2 model. Intuitively, higher value of NLL signifies that token is un- likely occur given the past context, which should hold true in case of noisy tokens with clean past. context.
4.2 Quality recovery after noise filtering
To remove noise from the input, we simply remove all sentences that have an OOD score greater than a threshold, and then evaluate how much output qual- ity gets recovered after this. We set the threshold of OOD score for filtering to be the 99 percentile value of the OOD scores computed for sentences in the clean version of the dataset (without any noise). The chosen percentile is set to be this high to minimize false positives which can lead moval of useful non-noisy information from the input. Since the threshold is computed using only the clean dataset and the model trained on that, we do not need any prior information about the noise
SGPT2(a)g) = j.
log pa (
(5)
where Pa(#x/000 (2)))) is the probability assigned by the GPT-2 model to token I'k given previous tokens.
To calculate performance of models at noise de- tection, we compare the assigned OOD score for

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Method
Overall AUC Randomsent
Per-example AUC
Code
Emoji
URL
Code
Emoji
Randomsent
URL
LO-Tok
77.10
84.25
73.63
85.41
78.52
84.17
74.74
86.83
LO-Sent
88.04
88.83
85,43
95.66
89,46
87.94
87.00
96.08
Sent
89.37
82.73
90.65
90.64
91.70
82.80
93.83
93.64
GPT-2
78.20
55.29
81.19
62.44
77.90
54.96
80.00
60.71
Table 2: Performance of different methods for noise detection aggregated across datasets (using the base model size and 0.5 noise amount)
(similar to OOD score computation).
noise is filtered. The best approach turns out to be setting it be the 99 percentile of the clean data OOD scores, which produces different thresholds for different models, and leads to the highest av- erage gain in output quality among the strategies tried, with minimal cases of further degradation. Surprisingly, optimizing the threshold based on F1- score of noise detection on a validation set also reduces the output quality in many cases, suggest- ing that Fi-score may not be the best predictor for the quality of summary produced after filtering. We conduct noise filtering for each of our ex- perimental setups (all datasets, noise types and amounts, model sizes) with three thresholds O, 200 and 500 and compare the resulting change summary quality with the precision and recall of the noise detection in Figure 3b. We find that a precision lower than around 0.7 usually leads to drop in summary quality, even if the recall is nearly perfect suggesting that almost all noise has been removed, This suggests that precision is more im- portant than recall for improving summary quality.
We show the performance of noise filtering for different noise types, model sizes and datasets in Table 3. For succinctness, show the geomet- ric mean of the ROUGE-1,2 and L variants, and point the reader the Appendix (Table 6) for de- tailed results with individual variants ROUGE. After noise filtering, we can recover a large part of the drop in ROUGE scores that occurred due to the added noise. In cases of large drop such as the Randomsent noise type with XSUM and SAM- Sum datasets, we can recover 4-6 and 6-7 points respectively depending the model size (Table 3).
We also present aggregate trends of recovery output quality using our filtering approach in Figure 2c and 2d. We can see that we recover over half of the drop in the performance on 9 out of 16 combinations of datasets and noise types (Figure 2c), with the best performance observed XSUM and SAMSum datasets and the worst CNN/DaillMaiil. The method also succeeds in recovering performance across all model sizes (Figure 2d).
Investigating causes of loss in performance
We experimented with various thresholding strategies such as setting thresholds to be constant irrespective of the dataset or model (e.g. 0), or to be equal to different percentile value (other than 99%) of the OOD scores produced by the model used on clean data. We also tried choosing the optimal threshold based on Fl-score of noise de- tection on a hold-out validation set (assuming a scenario where we have access to labeled noisy samples). We tried 6 thresholding techniques in total, compared in Figure 3a. Setting constant threshold of 0 provides gains in some cases but in other cases makes the model outputs worse, due to filtering out useful non-noisy content. To prevent this, one can use very high threshold such 500 which practically eliminates cases of further drop in performance (Figure 3a), but the performance gains produced that case are small because less
There are two distinct mechanisms which can lead to worsening of generated summaries upon addi- tion of input noise. The first is the corruption of the encoder's representation of useful clean tokens. The encoder transformer uses self-attention over input tokens to generate their contextualized repre- sentations. In cases where noise present the input, self-attention can distort the encoder repre- sentations of clean tokens. The second mechanism is the distraction of the decoder such that it assigns zono attention to the noisy tokens' embeddings and this impairs its computation. Even if there is no corruption in the embeddings of clean tokens, the embeddings of noisy tokens can receive non-zero cross cross-attention from the decoder and influence its generation. neither of these two phenomenon oc-

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Model size
Clean
Code
Emoji
Randomsent
URL
Add
Filter
Add
Filter
Add
Filter
Add
Filter
XSum
Small
31.66
21.43
27.50
23.28
31.33
22.28
28.44
25.50
30.30
Base
35.18
27.64
32.01
30.03
34,49
26.28
32.32
26.87
33.97
Large
37.18
35.86
36.89
36.36
36.83
31.68
35.09
35.81
36.77
CNN-Dailymai
Small
31.96
25.27
23.37
31.24
31.46
30.01
30.38
29.69
30.39
Base
33.09
26.27
25.39
32.53
32.70
31.31
31.53
30.74
31.25
Large
33.44
29.60
30.99
33.11
33.02
31.97
32.36
32.03
32.67
Samsum
Small
37.96
33.00
36.80
36.83
36.73
28.11
35.18
34.17
37.31
Base
39.74
36.95
38.89
39.18
38.97
31.96
37.51
36.89
39.47
Large
41.63
38.80
40.91
41.46
41.42
31.85
38.58
39.19
40.81
Reddit-TIFU
Small
15.51
11.53
13.55
12.97
15.21
13.40
14.70
13.41
14.09
Base
17.54
12.16
14.55
13.33
14.42
14.18
16.62
15.71
16.23
Large
18.15
13.33
16.06
14.89
15.76
13.92
17.32
15.96
16.88
Table 3: ROUGE scores (geometric mean of 1/2/L) on clean input and changes when adding different kinds of noise, and after the noise is filtered out using Sent method (Noise amount: 0.5)
Thresholding strategy
200
500
0.95pc
0.99pc
optim-f1
XSum=code
1.96
5.22
3.08
0.80
3.83
5.16
+url+
3.38
3.70
0.50
2.58
4.29
4.07
XSum+emooi
4.21
2.98
0.47
4.01
4.33
4.14
0.8
XSum Sandomaaddmseent
4.33
Dallymail+-ood-
1.00
Precision
CNN-Dailymail
Petaset=NNisetype
Daliymall+emojji
0:15
CNN-Dailymaill+ anddensent
0.38
0.16
-0.16
0.33
-2.99
Samsum=code
13:27
1.200
1.30
13.00
2.62
2:57
Semsum++ul-
-6:45
0.91
1.35
-0.35
2:45
2:46
D.2
Samsum+enne emoji
6.28
0.75
0.37
1..29
0.11
27.02
Samsum-randomssn
-6.23
4.15
6:46
4.02
6.15
6:41
Reddit-TFFU+cdde
--2
0.0
Reddit-TIFU-urf
0.0
0.2
0.4
0.6
0.8
1.0
Recall
Raddit-TIFU
2.98
3:12
Reddit III U+rddommeent
1.96
(b) Precision and recall for noise de- tection across different filtering ex- periments with varying thresholds. with the resulting change in output quality
AVERAGE 22.06
2.08
1.32
0.92
2.28
0.6G
(a) Increase in summary quality after filtering with different thresholding approaches, for diffferent datasets and noise types.
Figure 3: Change output quality for different thresholding techniques (a) and its correlation with the precision and recall of noise detection (b). The changes in summary quality illustrated color (blue shows increase and red shows decrease, saturation denotes magnitude clipped to range [0,5])
cur, the generated summary the noisy and clean variants of any input would be the same. In this
section investigate the contribution of these factors the degradation of output quality.

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5.1 Are the clean token embeddings corrupted by the presence of noise?
mostly leads an increase in output quality com- pared to when the noisy token embeddings are not removed (Figure 5). The biggest improvements come for XSUM and SAMSum datasets, whereas for CNN/DailyMail dataset no improvement seen for any of the 4 noise types. Surprisingly, for the ReddicT1FU-long dataset with the URL and Ran- domsent noise types, removing the noisy tokens' embeddings decreases the ROUGE scores further, suggesting that retaining those embeddings is use- ful for the decoder.
We observe that the OOD scores of the clean tokens increase after addition of noise. In Figure 4, we shown an example of this for the XSUM dataset after adding Code noise, where the OOD scores are computed using the Sent method. This sug- gests that the distribution of clean tokens' embed- dings moves farther from the in-domain distribu- tion (learnt from clean in-domain data) relative to the background distribution (leart from C4 cor- pus), after adding noise, We observed this for dif- ferent datasets and noise types, although the extent of the increase in OOD scores varies across them.
The above ablation study highlights the necessity of running the encoder twice - once for comput- ing OOD scores to detect noise, and then again to compute the encoder representations of the input after removing noisy tokens. While one can save computation time by reusing the encoder embed- dings of the clean tokens computed during OOD scoring feed them the decoder for generation, results from the ablation suggest that this would give sub-optimal performance recovery (Figure 5).
0.00175
post-noise
nolse
0.00150
0.00125
Denaity
0.00100
0.00075
0.00550
Change in performance metric
000000
-2000
-1000
1000
2000
00D score
-4
Figure 4: Distribution of OOD scores of (i) clean to- kens before adding noise (ii) clean tokens after adding noise and (iii) noisy tokens after adding them (using base model size and 0.5 noise amount)
-6
amaji
-8
code
randomsent
XSum
CNN-Dailymail
Samaum
Reddit-TIFU
5.2
How much performance can be recovered by preventing distraction of the decoder?
Figure 5: Performance drops for different datasets and noise types, with the shaded area showing drops when the noisy tokens' encoder embeddings removed be- fore running the decoder (using the base model size and 0.5 noise amount)
We design an ablation experiment to measure how the performance drop would change there is no distraction of the decoder by embeddings of noisy tokens. Any drop in output quality in such as setup is attributable only to the corruption of the clean tokens' encoder representations. We remove the embeddings of the (ground truth) noisy tokens af- ter passing the noisy input through the encoder of the PEGASUS model, and then use the decoder to generate the summary using only the remaining embeddings (see Figure 6 in Appendix for detailed workflow). Since the removal is done after passing the whole input through the self-attention layers of the encoder, the clean tokens' embeddings al- ready distorted, and the decoder has generate the summary using these distorted embeddings. The only difference from the usual scenario that the decoder does not have to include the noisy tokens' embeddings the computation. We find that this
6 Conclusion and Future Work
In this work, we quantified the impact that noisy inputs can have on the output quality of summa- rization models, for a variety of datasets and noise types. We then proposed a method to detect and remove noise from the input without using any extra models, training, or prior information about noise types, and demostrated its efficacy. One di- rection for future work is to investigate what makes certain models more susceptible specific noise types. Another interesting direction would be carry out experiments for noise filtering with real- world noisy data rather than using synthetically generated noisy examples.

---

7 Limitations
Misha Khalman, Yao Zhao, and Mohammad Saleh. 2021. Forumsum: A multi-speaker conversation summarization dataset. In Findings of the Associ- ation for Computational Linguistics: EMNLP 2021, pages 4592-4599.
While we have used 4 different types of noise in our experiments, there can be more types of noise that can be encountered in real world. While a positive aspect of our noise filtering approach is that it can be applied for any unforeseen type of noise, evaluating its performance against all types of noise is infeasible. Due to the heavy compute requirements, we experimented with only one type of pretrained summarization model (PEGASUS), and it is yet to be seen how the results generalize with other models such as T5 (Raffel et al., 2020b) and BART (Lewis et al., 2020). Since our noise detection approach is not perfect, it carries the risk of removing useful information instead of noise. However, our experiments show that while false positives occur, the filtering almost always does more good than harm when applied noisy doc- uments (Figure 2c). Additionally, the user has an option to minimize the risk of false positives by increasing the threshold of OOD score used for filtering.
Byeongchang Kim, Hyunwoo Kim, and Gunhee Kim. 2018. Abstractive summarization of reddit posts with multi-level memory networks.
Byeongchang Kim, Hyunwoo Kim, and Gunhee Kim. 2019. Abstractive summarization of Reddit posts Proceed-
with multi-level memory networks. ings of the 2019 Conference of the North American Chapter of the Association for Computational Lin- guistics: Human Language Technologies. Volume (Long and Short Papers), pages 2519-2531, Min- neapolis, Minnesota. Association for Computational Linguistics.
Mike Lewis, Yinhan Liu, Naman Goyal, Mar- jan Ghazvininejad, Abdelrahman Mohamed, Omer Levy. Veselin Stoyanov, and Luke Zettlemoyer. 2020. BART: Denoising sequence-to-sequuncce pre- training for natural language generation, translation, and comprehension. In Proceedings of the 58th An- nual Meeting of the Association for Computational Linguistics, pages 7871-7880, Online. Association for Computational Linguistics.
References
Chin-Yew Lin. 2004, ROUGE: A package for auto- matic evaluation of summaries. In Text Summmariza- tion Branches Out, pages 74-81, Barcelona, Spain. Association for Computational Linguistics.
Xiuying Chen, Guodong Long, Chongyang Tao, Mingzhe Li, Xin Gao, Chengqi Zhang, and Xian- gliang Zhang. 2023. Improving the robustness of summarization systems with dual augmentation. In Proceedings of the 61st Annual Meeting of the As- sociation for Computational Linguistics ( (Volume 1: Long Papers), pages 6846 6657, Toronto, Canada. Association for Computational Linguistics.
Ailsa Meechan-Maddon. 2019. The effect of noise in the training of convolutional neural networks for text summarisation.
Shashi Narayan, Shay Cohen, and Maria Lapata. 2018. Don't give me the details, just the summary! topic- aware convolutional neural networks for extreme summarization. In 2018 Conference on Empirical Methods in Natural Language Processing, pages 1797-1807. Association for Computational Linguis- tics.
Bogdan Gliwa, Iwona Mochol, Maciej Biesek, and Aleksander Wawer. 2019. SAMSum corpus: A human-annotated dialogue dataset for abstractive summarization. Proceedings the 2nd Workshop on New Frontiers in Summurization, pages 70-79, Hong Kong, China. Association for Computational Linguistics.
Xing Niu, Prashant Mathur, Georgiana Dinu, and Yaser Al-Onaizan. 2020. Evaluating robustness input perturbations for neural machine translation. In Pro- ceedings of the 58th Annual Meeting of the Asso- ciation for Computational Länguistics, pages 8538- 8544.
Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Mil- tiadis Allamanis, and Mare Brockschmidt. 2019. Out-of-distributioon detection and selective genera- tion for conditional language models. arXiv preprint arXiv: 1909.09436.
Hongyan Jing, Daniel Lopresti, and Chilin Shih. 2003. Summarization of noisy documents: pilot study. In Proceedings of the HLT-NAACL 03 Text Summariza- tion Workshop, pages 25-32.
Denis Peskov, Joe Barrow, Pedro Rodriguez, Graham Neubig, and Jordan Boyd-Graber. 2019. Mitigating noisy inputs for question answering. arXiv preprint 1e08.v/908.02
Vladimir Karpukhin. Omer Levy, Jacob Eisenstein, and Marjan Ghazvininejad. 2019. Training on synthetic noise improves robustness to natural noise in ma- chine translation. In Proceedings of the 5th Work- shop on Noisy User-generated Text ( (W-NUT 2019), pages 42-47.
Colin Raffel, Noam Shazcer, Adam Roberts, Kather- ine Lee, Sharan Narang, Michael Matena, Yangi Zhou, Wei Li, and Peter J. Liu. 2020a. Exploring the limits of transfer learning with unified text-to- text transformer. Journal of Machine Learning Re- search, 21 (144)

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Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter Liu, et al. 2020b. Exploring the limits of transfer learning with unified text-to-text trans- former. J. Mach. Learn. Res., 21(140):1-67.
Jie Ren, Jiaming Luo, Yao Zhao, Kundan Krishna, Mo- hammad Saleh, Balaji Lakshminarayanan, and Pe- ter Liu. 2023.
detection and selective generation for conditional language mod- cls. In International Conference Learning Repre- sentations.
Abigail See, Peter Liu, and Christopher D. Manning. 2017. Get to the point: Summarization with pointer- generator networks. In Proceedings of the 55th An- nual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1073- 1083, Vancouver, Canada. Association for Computa- tional Linguistics.
Vaibhav Vaibhav, Sumeet Singh, Craig Stewart, and Graham Neubig, 2019. Improving robustness of ma- chine translation with synthetic noise. In Proceed- ings of the 2019 Conference of the North American Chapter of the Association for Computational Lin- guistics: Human Language Technologies. Volume I (Long and Short Papers), pages 1926-1920.
Jingging Zhang. Yao Zhao, Mohammad Saleh, and Pe- ter Liu. 2020. Pegasus: Pre-training with extracted gap-sentences for abstractive summarization. In In- ternational Conference Machine Learning, pages 13328-11399, PMLR.

---

A Appendix
Inpur tokans
A.1 Selection of shorter inputs to avoid truncation
Mocaay tokera
Encoder
In our experiments, we exclude those datapoints from the datasets which longer than certain threshold. This done to avoid any truncation of the input (including inputs with added noise) when feeding them into the model. Since adding noise to the imput increases its length, it may happen that some clean tokens might be pushed beyond the maximum allowed input length and hence removed when the input is truncated. In such a scenario, removing noisy tokens before feeding the sequence into the model would also cause such clean tokens be fed into the model again because they can now be accommodated within the input length limit. When measuring the benefit of noise filtering, the benefit from removal of noisy tokens would then be confounded with the benefit from such "resurrec- tion" of clean tokens. To avoid this we only retain those inputs in our datasets where the input length would be within limit even after addition of noise. Since the maximum noise amount we use in our experiments is 0.5, we only retain datapoints which have no more than half of the maximum allowed tokens to input into the model. (Table 4).
Eincoder embeddingg
umbadding gi only thean Inkoee
Decoder
Generated summary
Figure Workflow the ablation experiment where the decoder does not have process the noisy tokens' embeddings.
consisting of 50M, 200M and 500M parameters respectively. The experiments. We used TPUs for our experiments. The bulk of the compute expen- diture was spent on running inference on differ- ent models for generating summaries on various noisy and noise-filtered variants of the datasets. The runtime of each experiment varies with differ- ent factors such as model size and dataset size, with the overall estimate for the total compute used at about 700 TPU hours. For model training and sum- mary generation on the XSUM, CNN/DailyMaii and ReddirTIFU-long datasets, we used the same hyperparameters as used in the original PEGASUS paper (Zhang et al., 2020) and for SAMSum dataset we use the hyperparameters given in Khalman et al. (2021). We used bytefallback during tokenization to enable proper representation of all unicode char- acters, instead of using UNK tokens. We experi- mented with a variety of hyperparmeters pertaining to noise addition and filtering, summarized in Ta- ble Due to the heavy compute requirements, all our experiments are single run and we did not try multiple seeds for the random noise addition. We used the NLTK library for sentence tokenization and used the rouge_scue sccree package from Google Research to compute the ROUGE scores of sum- maries. The default hyperparameters were used in the ROUGE calculation.
Dataset
Count
Maxlen
Retention
XSUM
7516
66.5%
CNNi
2948
512
25.6%
RedditTIFU-long
2790
66.2%
SAMSum
686
256
83.8%
Table 4: Number of datapoints retained in the test set of datasets after removing inputs longer than maximum length (Maxlen)
A.2 Compliance with licenses
Among the artifacts used in this work, the PEGA- SUS model and the CNN//ailyMail dataset are distributed under Apache License 2.0, the XSUM and ReddiTTIFU-long datasets are distributed un- der the MIT License, and the SAMSum dataset is distributed under CC BY-NC-ND 4.0 License. We use these resources for non-commercial research purposes with proper attribution, which is allowed by all the above licenses.
A.3 Implementation details
'https://ww.. www..nlk..ooo org/
We used PEGASUS models (Zhang et al., 2020) of three different sizes - small, base and large,
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---

Hyperparameter
Values
Noise type
(Code, Emoji, URL, Randomsent)
Noise amount
{0.00, 0.05, 0.10, 0.25, 0.50) (Small(50M), Base( (10M). Large(500M))
Model size
Fixed noise filtering threshold Adaptive noise filtering threshold
0, 200, 500
9Spercentile, 99percentile, optimal-F'l(n validation set)
Table 5: Different hyperparmeters used for noise addition and noise filtering
wLend offeet wrg. block2 superints[idx, i. edgearr [idx, 2] (idg 3]]. McLaren executive director Zak Brown said he regarded the 17-year-old Englishnan as "a fabulous prospect". yield doteessurresell.reeee resources (inrrreiiieentiiierrese _sourcee_ 1f getextent:: Norris won two Formula Renault 2.0 titles last season and will move to the European Formula Three series in 2017. self. shutdoon_networr(... objectinfol "maag" ").. Triple world chanpion Lewis Hamilton and Red Bull's Hax
Figure Sample excerpt from an article from XSum dataset corrupted with code noise.

---

Variant
Noise type
1OUGE-1/2/L.
XSum
Small
Base
Large
Clean
23.35/20..44 35.73
47.03 23.72 39.05
48.92/ 25.65 40.95
Code
31.54 12.44 25.07
38.74 17.34 31.43
47.53 24.48 39.63
Emoji
31.79 / 15.10 26.29
40.08 / 20.32 33.24
47.86 / 25.0 40.16
Noisy
Randomsent
32.38 / 13.10/ 26.09
36.54/ 16,63 / 29.87
42.67 / 21.06/ 35.38
URL
36.47 / 15.45 29.42
37.56 / 16.91 / 30.55
47.37 / 24.45 39.64
Code
38.72 / 17.00 31.61
43.50 / 20.98 35.94
48.55 25.45 40.64
Emoji
42.94 / 20.24 35.38
46.04 23.29 38.27
48.41 25.41 40.61
Filtered
Randomsent
39.84/ / 17.81 /3..42
43.88 / 21.30 36.11
46.65 / 23.84 38.85
URL
41.86/ / 19.30 / 34.43
45.69 / 22.69 / 37.80
48.41 25.34 40.54
CNN-Dailymail
Small
Base
Large
Clean
/4.50/22.744 32.27
45.70 23.72 33.43
46.20 / 24.08 33.61
Code
36.74/ 16.58 / 26.50
38.54/ 17.22 27.32
42.23 20.32/ 30.23
Emoji
43.97 22.11 31.35
45.25 / 23.21 32.77
45.95 23.74/ 33.27
Noisy
Randomsent
42.63 / 21.02 30.16
44.09
22.17 31.40
44.81 / 22.75 32.07
URL
42.19/ 30.17
43.60 / 21.45 31.05
44.89 / 22.69 32.27
Code
33.64 15,60 24.33
36.66 16.97 / 26.31
43.45 / 21.88 / 31.46
Emoji
/ 22.27 31.68
45.30 23.40 33.00
45.84 / 23.68 33.17
Filtered
Randomsent
42.99 / 21.34 30.58
44.26 22.35 31.70
45.16 23.08 32.51
URL
42.77 21.27 / 30.87
43.89 21.77 31.65
45.34/ 23.43 32.83
Samsum
Small
Base
Large
Clean
50.56 25.66 42.16
51.73 27.80 43.64
$3.50 / 29.53 45.68
Code
44.81 21.32 37.62
48.32 25.29 41.30
50.24 26.85 43.29
Emoji
49.27 / 24.41 /41.54
50.75 / 27.37 43.30
53.31 / 29.25/ 45.70
Noisy
Randomsent
39.81 17.27 32.31
42.79 21.30 35.83
42.22 / 35.85
URL
46.46 22.25 38.60
48.31 25.22 41.21
50.51 27.57 43.24
Code
49.22 24.56 /41.24
50.70 / 26.94 43.00
52.43 28.87 45.23
Emoji
49.00 24.41 /41.42
50.49 27.25 43.03
53.32 / 29.21 45.64
Filtered
Randomsent
47.36 23.31 39,43
49.47 25.64 41.60
50.40 / 26.56/ 42.89
URL
49.65 25.16 41.58
51.29 27.63 43.39
52.56 28.70 45.07
Reddit-TIFU
Small
Large
Clean
24.06/7.81 19.86
26.74/ 9.20 21.95
27.45 9.65 22.56
Code
17.95 / 5.74 14.87
18.72/ 6,21 15.46
20.35 6.91 / 16.85
Emoji
20.25 6.47 16.65
20.09 7.14/ 16.51
22.51 77.90/ 18.55
Noisy
Randomsent
21.15 6.62 17.18
22.09 / / 18.08
21.47 7.09 / 17.73
URL
21.02 / 6.66 17.23
24.25 8.09 19.76
24.55 8.17 20.26
Code
20.98 / 6.83 17.37
22.24 7.58 18.27
24.31 / 8..66 20.15
Emoji
23.49 / 7.71 19.42
21.95 7.59 17.99
23.79 8.40 19.59
Filtered
Randomsent
23.05 7.32 18.81
25.57 8.64 20.78
26.37 9.12 21.59
URL
21.96 / 7.10 17.94
24.88 8.44 20.37
25.74 8.84 21.14
Table 6: ROUGE scores clean input and changes when adding different kinds of noise, and after the noise is filtered out using the Sent method based OOD scores (Noise amount: 0.5)


Reviews:
[{"review_id": "w9RPyuaL1j", "reviewer": "Reviewer_vmhZ", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your insightful comments, and for acknowledging the importance of understanding and improving noise-resilience of models.\n\nWe agree with your point that it is important to make the method work in real-world noisy data. While the choices of noise types in our study (e.g. URLs) were actually inspired by the plausibility of their occurrence in real world noisy data, we found it infeasible to run large-scale experiments with a real-world noisy dataset due to the following reasons:\nThe types of noise that can be encountered by systems deployed in the real world are vast and difficult to capture. Additionally, since the occurrence of noise is rare in natural sources, curating a dataset of real-world noisy inputs would require a significant amount of work which would be a different paper in its own right and outside the scope of our paper. One example is that of “MTNT: A testbed for machine translation of noisy text ”(Michel et al. 2018)  which creates a benchmark with noisy inputs for the machine translation task. On the other hand, for summarization, to the best of our knowledge no such benchmark of noisy inputs exists. This is a recurring challenge in NLP which compels researchers to use synthetic noise due to lack of real-world noisy samples, and multiple published works in the past have used it in machine translation, question answering and summarization (some references provided in lines 127-131). \n\nHence, to shine light on the unexplored question of how modern transformer-based summarization models would react to noise, we did our best by simulating plausible kinds of noise that are likely to come up in different usage scenarios. For example, we included URLs as they can be frequently shared in chats or articles and could be included in the model input. However, we acknowledge the fact that real-world noise can be diverse and different from the synthetic noise we use, and for that reason we chose to design a noise-filtering approach that works without any prior knowledge about the kind of noise that is present in the input and can recover performance across noise types (Figure 2c).", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "This paper conducts an in-depth study of the impact of noisy inputs on the summarization models. The authors also present strategies to reduce the impact of such noises and provide an analysis of the cause of performance loss from the input noise. The observations and conclusions of this paper can be useful for understanding the summarization model performance when used in noisy/real-life application scenarios, and the noise-reducing strategies proposed in this paper can also be useful for future work.", "reasons_to_reject": "The major concern I have is that the input noise is synthetically injected into the datasets in this paper. This mainly leads to two questions/concerns: 1. whether the synthetic noise pattern is similar to the noise pattern from the input of real-world data. 2. whether the noise-reducing strategies proposed in this paper for synthetic noise can also be successfully applied to real-world noisy data. Therefore, personally, I think the observations and conclusions made in the paper are not trustworthy enough when we are considering their effectiveness in real-world application scenarios, and one important missing part of this paper is performing analysis/study on real-world noisy data. For example, since the widely-used CNN/DailyMail and XSum datasets are from news articles on the websites of CNN/BBC, the HTML files that contain the news articles can be a natural source of such noisy input data samples. ", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper studies the impact of noisy inputs on summarization models and the strategies to remove such noises and provides an analysis of the causes of performance loss from the noisy inputs w.r.t. the encoder and decode of the summarization models.", "excitement": 4, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "Dwfajg1Anw", "reviewer": "Reviewer_PApz", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for reviewing our draft and championing our work! We were pleased to see that you appreciated the novelty of the paper’s theme: studying the effect of input noise on modern summarization systems.\n\nFor the evaluation, we used only PEGASUS models due to compute resource constraints since we ran a large number of experiments to explore many variations of hyperparameters involved like: (1) model size, (2) noise type (3) noise amount (4) noise filtering strategy. However, exploring the behavior of other pretrained models such as T5 models could be an interesting future direction. Our intuition is that most transformer based models such as T4, BART etc should show similar results since they all have the same architecture and only differ in pretraining strategies, but it is also possible that some pretraining strategies (especially denoising-based pretraining) may lead to higher noise robustness.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "This is an interesting and novel task, with practical significance. \n\nA thorough study, showing promising results.\n\nA well written paper that could be of interest to the industry-centered NLP community.\n", "reasons_to_reject": "Limited novelty.\n\nThe evaluation could be expanded, only PEGASUS models are used on the abstractive summarization task.", "questions_to_authors": "", "paper_topic_and_main_contributions": "The paper studies a practical and novel topic: affect of input noise on abstractive summarization. The study 1) quantifies the impact of various kinds of noise on pretrained Transformer-based summarization models, 2) shows that this noise can be detected using adaptations of recently proposed out-of-distribution detection method 2) examine how different parts of the model’s computation are affected by the introduction of input noise.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "S38A8Z1ZGh", "reviewer": "Reviewer_hpax", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for reviewing our work and providing us with your valuable thoughts. We were delighted to see that you found the empirical results in the submission to be valuable.\n\nWe agree that the amount of noise in real-world data may not be as high as what is used in some experiments in the paper. Although we did include experiments with different noise amounts as low as 5% (Figure 2a,b), we used a higher noise amount in our experiments with noise-filtering simply because low noise amounts did not lead to any significant drop in model performance to recover via filtering. However, we believe even demonstrating that low noise amounts lead to minimal damage to model performance is by itself a novel contribution of our work, since to the best of our knowledge there aren’t any studies probing noise-resilience of modern pretrained transformer based summarization models (which are the de-facto standard in today’s NLP world). \n\nRegarding simpler approaches for noise filtering, we weren't able to grasp the proposed approach in the review, and we request you to please elaborate if possible. Based on what we could gather, it seems that maybe you are proposing a pattern-matching based approach to filtering noise, such as filtering out URLs using http prefix or emojis by looking them up from an exhaustive database of emojis. However, there are two problems with this approach:\n\n(1) While pattern-matching can work extremely well for specific noise types, they would need to be handcrafted for different noise types and so it is not a scalable approach. In contrast, our noise filtering approach is generic, works without any prior knowledge of the noise type, and recovers performance across different kinds of noise (Figure 2c).\n\n(2) It is not possible to come up with simple pattern matching approaches for some noise types. For example, the Randomsent noise type in the paper introduces randomly sampled sentences from the C4 corpus as noise, and since it can be any sentence, it is almost surely impossible to come up with a pattern matching approach to distinguish them from the clean sentences. However, since our approach uses contextualized encoder representations for noise detection, we can detect Randomsent noise type with an overall AUC score of 90.65 (Table 2), and after filtering we can recover more than half of the drop in summary quality in 3 out of 4 datasets we experimented with (Figure 2c).", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. The presence of noises in datasets is a real concern, and the model introduced effectively addresses the resulting performance dips.\n\n2. The paper investigates the reasons behind performance drop, specifically highlighting the \"corruption of the encoder's representation of clean tokens\" and the \"distraction of the decoder due to non-zero attention assigned to the noisy tokens’ embeddings.\"\n\n3. Empirical results presented offer valuable insights into how various noise types impact models of different sizes.", "reasons_to_reject": "1. One of the major concerns lies in the way the noisy experimental dataset has been manually constructed. It can be argued that real-world datasets, especially renowned ones like CNN/DM and XSum, might not exhibit as much noise as has been simulated for this study. This discrepancy could potentially affect the generalizability and applicability of the results.\n\n2. The paper employs a model from Ren et al. (2023) to address the noise issue. However, a more straightforward engineering approach might have sufficed, particularly for common noises like URLs starting with \"http\" and emojis. These simpler methods could be more efficient and easier to implement in practical applications. Even without identifying the specific categories of noises, these disturbances can still be removed right from the beginning.\n\n3. The paper's results display inconsistency in some scenarios, with performance sometimes witnessing a decline. Notably, there's a drop from 26.27 to 25.39 for the base model after code filtering. The paper would benefit from providing insight into these unexpected outcomes, as currently, readers are left without an understanding of these discrepancies.", "questions_to_authors": "", "paper_topic_and_main_contributions": "The study offers an empirical examination of the significant performance decline due to various input noises across diverse datasets and model sizes. In response, the paper introduces a method that detects and eradicates such noise during the inference phase, eliminating the need for additional training. This method effectively counters the degradation in performance.", "excitement": 3, "reproducibility": 2, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "Improving the Robustness of Summarization Systems with Dual Augmentation, ACL 2023"}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper studies the impact of noise in input for summarization models, and proposes methods to automatically detect and remove such noise. This is quite a relevant and exciting area for research. The main limitation pointed out by multiple reviewers is the synthetic nature of the noise injected into documents, which raises concerns that the proposed method to detect noise may not generalize to real noise distributions. Already, the noise detection experiments are conducted on a pretty high noise level which the authors agree is not the same as noise levels. I would encourage the authors to collect a small test set of examples with 'real noise' to show generalizability of their approach.
Decision: Accept-Findings