# Efficient Few-Shot Fine-Tuning for Opinion Summarization

Arthur Bražinskas1 Ramesh Nallapati2 Mohit Bansal2,3 Markus Dreyer2
1ILCC University of Edinburgh 2Amazon
3UNC Chapel Hill
<EMAIL_ADDRESS>{rnallapa, mobansal<EMAIL_ADDRESS>
###### Abstract

Abstractive summarization models are typically pre-trained on large amounts of
generic texts, then fine-tuned on tens or hundreds of thousands of annotated
samples. However, in opinion summarization, large annotated datasets of
reviews paired with reference summaries are not available and would be
expensive to create. This calls for fine-tuning methods robust to overfitting
on small datasets. In addition, generically pre-trained models are often not
accustomed to the specifics of customer reviews and, after fine-tuning, yield
summaries with disfluencies and semantic mistakes. To address these problems,
we utilize an efficient few-shot method based on adapters which, as we show,
can easily store in-domain knowledge. Instead of fine-tuning the entire model,
we add adapters and pre-train them in a task-specific way on a large corpus of
unannotated customer reviews, using held-out reviews as pseudo summaries.
Then, fine-tune the adapters on the small available human-annotated dataset.
We show that this self-supervised adapter pre-training improves summary
quality over standard fine-tuning by 2.0 and 1.3 ROUGE-L points on the Amazon
and Yelp datasets, respectively. Finally, for summary personalization, we
condition on aspect keyword queries, automatically created from generic
datasets. In the same vein, we pre-train the adapters in a query-based manner
on customer reviews and then fine-tune them on annotated datasets. This
results in better-organized summary content reflected in improved coherence
and fewer redundancies.

##  1 Introduction

Figure 1: Illustration of the proposed approach. In Stage 1, all parameters of
a large language model are pre-trained on generic texts (we use BART). In
Stage 2, we pre-train adapters (5% of the full model’s parameters) on customer
reviews using held-out reviews as summaries. In Stage 3, we fine-tune the
adapters on a handful of reviews-summary pairs.

Online reviews play an important role in purchasing decisions we make. They
inform us about customer experiences – what aspects users like and dislike,
and ultimately, whether a product or service is worth buying. Although
significant progress has been made in supervised summarization in non-
subjective single-document context, such as news articles (Rush et al., 2015;
Nallapati et al., 2016; See et al., 2017; Bražinskas et al., 2021b), modern
deep learning methods rely on large amounts of annotated data, but these are
not readily available in the review or opinion summarization domain and are
expensive to produce. A key obstacle making such annotation expensive is that
annotators need to consider multiple texts when writing a summary, which can
be tens or even hundreds for realistic settings. Hence, most available
datasets have human-written summaries for less than 100 products.
The lack of sufficiently large annotated datasets led to a variety of
unsupervised abstractive models (e.g., Copycat (Bražinskas et al., 2020b),
MeanSum (Chu and Liu, 2019); DenoiseSum (Amplayo and Lapata, 2020)) that are
trained on large collections of unannotated customer reviews. However, as the
models are never exposed to actual summaries, they cannot capture their
expected characteristics. This results in generated summaries mimicking the
informal style of customer reviews and containing hallucinations and
unimportant details. These limitations were addressed by few-shot methods
learning from a handful of human-written summaries (Bražinskas et al., 2020a;
Oved and Levy, 2021). The first proposed few-shot learning model, FewSum
(Bražinskas et al., 2020a), relies on various static features capturing
differences between a customer review and a summary. While that model is more
robust to overfitting, such features require manual, domain-specific
engineering and can be sub-optimal for capturing correspondences between texts
on the semantic level. We propose a simpler approach – AdaSum – which is based
on adapters (Houlsby et al., 2019; Bapna et al., 2019). As we explain next,
the adapters are pre-trained on customer reviews in a task-specific manner,
and subsequently fine-tuned on gold summaries.

#### Adapters.

We utilize a pre-trained model with powerful language understanding and
generation abilities in combination with a parameter-efficient fine-tuning
method – adapters. As was shown in recent studies, this method is also robust
to overfitting in low-resource settings (He et al., 2021). In this way, a
large pre-trained model, BART (Lewis et al., 2020), in our case, remains
frozen, and only small modules (0.6% - 5% of the model parameters) are
optimized. This effectively retains acquired knowledge in the pre-trained
language model (PLM) without specialized training objectives as in RecAdam
(Chen et al., 2020). However, available annotated data is not sufficient for
learning in-domain specifics and results in summaries with subtle semantic
mistakes. As explained next, we reduce these semantic mistakes by pre-training
adapters on customer reviews.

#### Self-supervised Pre-training.

Language models are pre-trained with generic objectives (e.g., single-document
denoising) and rarely on in-domain data, such as customer reviews.
Consequently, this makes them less attuned to in-domain specifics as these are
hard to learn from a handful of summaries. This often results in subtle
semantic mistakes. For instance, in Table 1, PASS (Oved and Levy, 2021)
incorrectly concludes that thin material implies poor quality. To address this
issue, we learn in-domain specifics from customer reviews. Concretely, we
employ a self-supervised pre-training method: For any given product without a
human-written summary, we predict one of the given reviews by conditioning on
$N$ other reviews with the highest lexical overlap in the leave-one-out
fashion (Bražinskas et al., 2020b). As the standard training of PLMs is
storage and memory inefficient (Mahabadi et al., 2021), we pre-train adapters
only; see Stage 2 in Fig. 1. Afterwards, we fine-tune them on a small number
of annotated reviews-summary pairs (< 100 pairs), see Stage 3 in Fig. 1. All
in all, our method combines the general text generation and understanding
abilities of the PLM with in-domain knowledge directly related to the end
task.

#### Content Planning.

Well-organized content in summaries is easier to follow and thus improves user
experience. However, the lack of annotated data makes it challenging to learn
a desired content structure. For example, in Table 1, FewSum’s summary does
not end after a concluding phrase ‘Other than that, it’s a great top.’ While
the state-of-the-art model (PASS) addresses this issue by ranking multiple
generated candidates with a specialized coherence model, we propose a simpler
solution – AdaQSum – that capitalizes on text planning (Hua and Wang, 2019;
Moryossef et al., 2019). Specifically, we allow the model to plan ahead by
providing an intermediate summary representation in the form of a query
consisting of aspect keywords. As we show, this results in more coherent text
patterns with fewer redundancies. Moreover, it can be useful for personalized
summaries, better reflecting user interests.

FewSum |  This tank top is well made, fits well, and is comfortable to wear. The only thing is that it runs a little small, so order a size up from what you normally wear. Other than that, it’s a great top. It’s well made and it looks like it will last a long time. Love it!
---|---
PASS |  This is a basic tank. The photo shows it going well past the models hips. However, the material used to make it this long is thin and therefore not good quality. It is also thinner than other tanks on the market but is still comfortable to wear.
AdaQSum |  This is a basic tank top that fits well and is comfortable to wear. The color is great and the length is long enough to wear with leggings. The quality of the product is good.
Reviews |  … This is a basic tank … || … this tank fits like a normal tank top, not any longer … I could wear it with leggings … || … It is THIN and runs SMALL … It fits tight and is NOT long like in the picture … || The tank fit very well and was comfortbale to wear. I’ve bought much higher quality tanks … || … it is listed as a ’long’ tank top and the photo even shows it going well past the models hips, however I’m short and the tank top is just a normal length. || … They were a lot thinner than I like … || Every women should own one in every color. Just feels quality I don’t know how else to explain it … || … They are long enough that the color peeks out from under my tops. Looks cute.
|
Table 1: Generated summaries for an Amazon product by baseline models (FewSum
and Pass) and our approach (AdaQSum). Colored words indicate aspect keywords
that were part of the query. The special marker ‘||’ separates truncated
reviews.

#### Result Highlights.

We evaluate the proposed models in terms of automatic metrics and human
efforts. We find that pre-training and fine-tuning of adapters leads to more
than 2.0 and 1.3 ROUGE-L points improvement over fine-tuning the entire model
on Amazon and Yelp datasets, respectively. We also find that our pre-trained
and fine-tuned query-based model improves ROUGE-L scores by more than 2.7 and
0.9 ROUGE-L points over PASS, on Amazon and Yelp datasets, respectively, and
is more preferred by humans. We further demonstrate that the query-based model
(AdaQSum) substantially improves coherence and reduces redundancies in
generated summaries.

In summary, our contributions are as follows:

  * •

We propose a self-supervised pre-training method to learn in-domain knowledge
by adapters that alleviate catastrophic forgetting;

  * •

We propose, to the best of our knowledge, the first aspect-based abstractive
opinion summarizer learned from a few annotated samples;

  * •

We substantially increase summary coherence using the query-based approach;

  * •

We show that self-supervised pre-training significantly improves performance
on the query-based task;

  * •

We demonstrate state-of-the-art results on two primary benchmarks in automatic
and human evaluation.111Our code and associated artifacts is publicly
available at https://github.com/amazon-research/adasum.

##  2 Approach

###  2.1 Opinion Summarization Tasks

In this work, we consider two tasks of customer review summarization. The
first one is generic summarization (Chu and Liu, 2019; Bražinskas et al.,
2020a), where the aim is to produce a summary that covers overall opinions in
input reviews. Formally, given $N$ input reviews $r_{1:N}$, the task is to
predict word-by-word the summary $s$:

$\mathcal{L}(s,r_{1:N};\theta)=\sum_{t=1}^{T}\log
p_{\theta}(s^{t}|s^{1:{t-1}},r_{1:N}).$

In the second task, query-based summarization, we assume that the user
provides a query $q$ consisting of aspect keywords, such as ‘bluetooth,’
‘resolution,’ and ‘battery life.’ In turn, a summarizer should generate a
summary reflecting customer opinions in $r_{1:N}$ about these aspects.
Formally, given a pair of input reviews and query ($r_{1:N}$, $q$), the task
is to predict word-by-word the summary $s$:

$\mathcal{L}(s,r_{1:N},q;\theta)=\sum_{t=1}^{T}\log
p_{\theta}(s^{t}|s^{1:{t-1}},r_{1:N},q).$

Unfortunately, abstractive opinion datasets with annotated aspect queries are
unavailable in the domain. To mitigate this problem, we follow Ni et al.
(2019) and create queries by extracting fine-grained aspect keywords from
available generic summaries. Specifically, we utilize the model proposed by
Zhang et al. (2014) to build a fine-grained aspect lexicon from review
datasets. Further, we use simple rules to determine which aspects appear in
summaries; see an annotated summary in Table 2. At test time, we follow the
intuition that a summary should reflect common opinions and create a query
from $K$ most frequent aspect keywords in input reviews. The workflow is
illustrated in Fig. 2.

Figure 2: Illustration of the query-based summarizer that inputs reviews and a
text query consisting of aspects, such as ‘volume,’ ‘price,’ and ‘bluetooth.’
The query is automatically created from gold summaries in training and reviews
in test time.

###  2.2 Model

Our model is based on the Transformer (Vaswani et al., 2017) encoder-decoder
architecture initialized with BART (Lewis et al., 2020). We adopt the same
encoder as in (Raffel et al., 2020; Oved and Levy, 2021) where reviews are
concatenated before encoding.222We experimented with the independent review
encoding as in Bražinskas et al. (2020b). However, the results were slightly
worse. This allows us to capture product-level features and leverage
commonalities across reviews during encoding. For query-based summarization,
we concatenate a query and reviews while indicating boundaries with special
markers. In this way, the encoder can contextualize aspect keywords and focus
on salient review fragments reflecting these aspects.

###  2.3 Adapters

In training, a large pre-trained model remains frozen and only tiny neural
networks called adapters (Houlsby et al., 2019) are optimized. These modules
are injected into the transformer layers (both encoder and decoder). Formally,
given the input hidden vector $h$, the output vector $\hat{h}$ is calculated
as shown below:

$\hat{h}=f_{2}(\tanh f_{1}(h))+h.$

The functions $f_{1}(\cdot)$ and $f_{2}(\cdot)$ are the down- and up-
projection layers. At each transformer layer, two adapters are inserted right
after the self-attention and the feed-forward layers, respectively. These
modules consist of substantially fewer parameters than the language model,
usually around 3% - 5%. Recent studies have shown that adapters are less prone
to overfitting (He et al., 2021) and are more memory-efficient in training
(Mahabadi et al., 2021). Finally, as the pre-trained model remains frozen, it
retains all the prior knowledge for text understanding and generation. This
effectively alleviates catastrophic forgetting (Goodfellow et al., 2013;
Kemker et al., 2017) without modifying the training objective as in RecAdam
(Chen et al., 2020; Yu et al., 2021). We refer to our approaches as AdaSum and
AdaQSum for generic and query-based summarization, respectively.

The cover offers durable protection for the MacBook, the retractable tilt
stands offer protection for the wrists.
---
The keyboard cover can take some effort to fit properly, and adjustment to its
feel may take time.
However, free and fast shipping make up for this one potential issue.
Table 2: Automatically annotated Amazon summary with fine-grained aspect
keywords (underlined italic).

###  2.4 Self-supervised Pre-training

Language models, initially pre-trained on generic text corpora, are often not
accustomed to in-domain specifics. Unsurprisingly perhaps, a wide range of
product-related specifics cannot be learned from a handful of annotated
summaries during fine-tuning. Consequently, this can result in subtle semantic
mistakes in generated summaries. We will discuss this problem and provide
examples in Sec. 5.4. Furthermore, query-based summarization is even more
challenging for learning than generic summarization. To be useful in practice,
the summarizer should rely on the provided query after fine-tuning. However, a
handful of annotated samples might be insufficient to learn this dynamic. We
will analyze this problem in Sec. 5.1. To alleviate these two problems, we
leverage unannotated customer reviews to construct synthetic datasets for pre-
training.

#### Synthetic In-Domain Pre-Training Dataset.

From a group of product reviews, we randomly sample one review as a pseudo
summary $s$ and select $N$ reviews as input ($r_{1:N}$).333We also
experimented with selecting pseudo summaries without personal pronouns –
written in the formal style (Bražinskas et al., 2020a). However, we did not
observe significant improvements. We select $N$ input reviews covering the
content of the summary $s$ – that have the highest ROUGE-1 F scores. Following
the naming convention (Bražinskas et al., 2020b), we refer to this as leave-
one-out pre-training (l1o). To closely resemble query-based summarization, we
create aspect queries from pseudo summaries. Specifically, we leverage the
aspect lexicon by matching summary keywords; in the same way as was explained
in Sec. 2.1. In practice, we expect queries to have at least one aspect
keyword. Therefore, we remove all pre-training pairs where the pseudo summary
has no aspect keywords.

##  3 Experimental Setup

###  3.1 Data

To create synthetic datasets, we used customer reviews from Amazon (He and
McAuley, 2016) and Yelp.444https://www.yelp.com/dataset Following Bražinskas
et al. (2020a), we selected 4 categories: Electronics; Clothing, Shoes and
Jewelry; Home and Kitchen; Health and Personal Care. We pre-processed the
datasets by removing all reviews that are shorter than 20 words and longer
than 120 words and evened the number of pairs in both datasets. Further, we
used Amazon and Yelp gold summaries from (Bražinskas et al., 2020a) where each
product/business has 3 references and is paired with 8 reviews. Gold and
synthetic dataset statistics555For the query-based setup, we removed all
instances where targets had no aspects. are presented in Table 3.

| Amazon | Yelp
---|---|---
Split | Gold | Synthetic | Gold | Synthetic
Train | 84 | 70,144 / 59,963 | 90 | 70,144 / 68,499
Valid | 36 | 7,900 / 6,810 | 90 | 7,900 / 7,724
Test | 60 | - | 120 | -
Table 3: Source-target pair numbers for Amazon and Yelp, both gold and
synthetic. Each pair has 8 source reviews. Generic and query-based pair
statistics are separated by ’/‘.

###  3.2 Baselines

LexRank (Erkan and Radev, 2004) is an unsupervised extractive graph-based
model that selects sentences based on graph centrality. Sentences represent
nodes in a graph whose edges are weighted with tf-idf.

MeanSum (Chu and Liu, 2019) is an unsupervised abstractive summarization model
which treats a summary as a structured latent state of an auto-encoder trained
to reconstruct reviews of a product.

Copycat (Bražinskas et al., 2020b) is the state-of-the-art unsupervised
abstractive summarizer with hierarchical continuous latent representations to
model products and individual reviews.

FewSum (Bražinskas et al., 2020a) is a few-shot framework where lexical
features are used to differentiate between customer reviews and summaries. In
the fine-tuning phase, features leading to generation of summaries are
searched.

PASS (Oved and Levy, 2021) is based on a pre-trained T5 model (Raffel et al.,
2020) that is further fine-tuned on gold summaries. At inference, the model’s
input is perturbed to generate multiple candidates. These candidates are
further ranked by a separate model based on coherence and fluency to select
the best one.

We fine-tuned the full BART model (Full) for a fair comparison, with and
without the leave-one-out pre-training. We also employed a number of simple
summarization baselines. First, the clustroid review was computed for each
group of reviews as follows. We took each review from a group and computed
ROUGE-L with respect to all other reviews. The review with the highest ROUGE
score was selected as the clustroid review. Second, we sampled a random review
from each group to be used as the summary. And lastly, we constructed the
summary by selecting the leading sentences (Lead) from each review of a group.

###  3.3 Experimental Details

We used a standard Transformer encoder-decoder (Vaswani et al., 2017), pre-
initialized with BART large (Lewis et al., 2020), consisting of 400M
parameters. We used two adapter sizes – 0.6% and 5% of the full model’s
parameters. All input reviews were concatenated, following Raffel et al.
(2020); Oved and Levy (2021). For parameter optimization, we used Adam (Kingma
and Ba, 2014), and summary generation was performed via the beam search of
size 5 and with 3-gram blocking (Paulus et al., 2017). We used ROUGE-L as the
stopping criterion on the end task, and perplexity (PPL) for pre-training. The
learning rate for most experiments was set to 5e-5. Aspect lexicons for query-
based summarization contained 2809 and 4013 fine-grained aspects for Amazon
and Yelp, respectively. In pre-training and fine-tuning, we shuffled aspects
to break temporal dependencies. For fine-tuning on Yelp, we also found it
useful to exclude summary aspect keywords that do not appear in input reviews.
This approximately matched the number of aspect keywords for Amazon and Yelp.
At test time, we selected up to 6 and 5 most frequent aspects for Amazon and
Yelp, respectively. All computations were performed on an 8-GPU p3.8-xlarge
Amazon instance.

##  4 Results

###  4.1 Automatic Evaluation

|  | Amazon | Yelp
---|---|---|---
| Params$\downarrow$ | PPL$\downarrow$ | R1$\uparrow$ | R2$\uparrow$ | RL$\uparrow$ | PPL$\downarrow$ | R1$\uparrow$ | R2$\uparrow$ | RL$\uparrow$
|
Clustroid | - | - | 27.16 | 3.61 | 16.77 | - | 28.90 | 4.90 | 18.00
Lead | - | - | 27.00 | 4.92 | 14.95 | - | 26.20 | 4.57 | 14.32
Random | - | - | 25.00 | 3.82 | 15.72 | - | 21.48 | 2.59 | 13.87
Unsupervised
LexRank (Erkan and Radev, 2004) | - | - | 27.72 | 5.06 | 17.04 | - | 26.96 | 4.93 | 16.13
MeanSum (Chu and Liu, 2019) | 25M | - | 26.63 | 4.89 | 17.11 | - | 27.50 | 3.54 | 16.09
Copycat (Bražinskas et al., 2020b) | 25M | - | 27.85 | 4.77 | 18.86 | - | 28.12 | 5.89 | 18.32
Few-shot
FewSum (Bražinskas et al., 2020a) | 25M | - | 33.56 | 7.16 | 21.49 | - | 37.29 | 9.92 | 22.76
PASS (Oved and Levy, 2021) | 440M | - | 37.43 | 8.02 | 23.34 | - | 36.91 | 8.12 | 23.09
Full (100%) | 400M | 17.87 | 37.22 | 9.17 | 23.51 | 12.87 | 37.40 | 10.27 | 23.76
Full (100%) + l1o | 400M | 16.90 | 37.67 | 10.28 | 24.32 | 12.40 | 36.79 | 11.07 | 25.03
AdaSum (0.6%) | 2.6M | 13.45 | 38.49 | 9.84 | 24.37 | 11.94 | 37.55 | 10.11 | 24.08
AdaSum (0.6%) + l1o | 2.6M | 12.06 | 38.94 | 10.63 | 24.95 | 11.23 | 37.78 | 11.31 | 24.04
AdaSum (5%) | 21.3M | 16.30 | 38.15 | 9.18 | 23.17 | 12.50 | 38.12 | 10.89 | 24.11
AdaSum (5%) + l1o | 21.3M | 12.03 | 39.78 | 10.80 | 25.55 | 11.11 | 38.82 | 11.75 | 25.14
|  |  |  |  |  |  |  |  |
Table 4:  Test set ROUGE F1 scores on gold Amazon and Yelp datasets for
generic review summarization. l1o stands for leave-one-out pre-training. We
also provide the total number of trainable parameters.

Table 4 shows results on the Amazon and Yelp test sets for generic
summarization. It shows ROUGE F1 scores (Lin, 2004) as a standard measure of
informativeness666For consistency with previous works, we used the same Python
package (https://github.com/google-research/google-research/tree/master/rouge)
and perplexity (PPL) as a measure of confusion.

First of all, the results indicate the superiority of adapters over full fine-
tuning and state-of-the-art few-shot models on both datasets. As was observed
in He et al. (2021), adapters are less prone to overfitting, which is
especially beneficial in few-shot settings. Second, we observe a significant
improvement in ROUGE scores when pre-trained models are further trained using
l1o. This signifies the importance of learning in-domain specifics before
fine-tuning. We also observe that adapters are more effective on the Amazon
dataset, which is more challenging as indicated by higher perplexity
(PPL).777Training sets are of similar sizes, i.e., 84 and 90 summaries on
Amazon and Yelp, respectively We hypothesize that the pre-trained language
model (BART) is more accustomed to restaurant- than product-related texts.
Moreover, larger adapters (5%) tend to overfit on the small number of
annotated instances, and l1o pre-training helps substantially, as indicated
both by ROUGE scores and PPL. We provide example generated summaries in the
Appendix.

###  4.2 Human Evaluation

#### Coherence Improvement.

|  | R1 | R2 | RL | unique 1-gram (%) | unique 2-gram (%)
---|---|---|---|---|---|---
Amazon | AdaSum (5%) + l1o | 39.78 | 10.80 | 25.55 | 67.72 | 80.83
AdaQSum (5%) + l1o | 38.53 | 10.52 | 26.06 | 69.38 | 82.57
Yelp | AdaSum (5%) + l1o | 38.82 | 11.75 | 25.14 | 62.26 | 76.55
AdaQSum (5%) + l1o | 36.79 | 10.06 | 23.99 | 65.74 | 79.88
|  |  |  |  |  |
Table 5: Comparison of the query-based and generic summarizers on test sets.
Unique n-grams were computed in generated summaries.

As was observed in (Oved and Levy, 2021), opinion summarizers sometimes
generate incoherent summaries. We hypothesized that a query should allow the
model to plan ahead of time and thus generate more coherent and less redundant
texts. To test the hypothesis, we compared 5% adapter-based models with and
without the query; both were pre-trained via l1o. We performed human
evaluation in terms of coherence and non-redundancy via Best-Worst Scaling
(BWS) (Louviere and Woodworth, 1991; Louviere et al., 2015). BWS has been
shown to produce more reliable results than ranking scales (Kiritchenko and
Mohammad, 2016).

For each Amazon test set entry and criterion, we asked three independent
workers on Amazon Mechanical Turk (AMT) to select the best and worst summary.
For each criterion, a system’s score is computed as the percentage of times it
was selected as best, minus the percentage of times it was selected as worst
(Orme, 2009). The scores range from -100 (unanimously worst) to +100
(unanimously best). For more details, please refer to Appendix 10.2.

First, the results indicate that the summaries generated by AdaQSum are
substantially more preferred to AdaSum in terms of coherence and non-
redundancy. Namely, +13.73 vs -30.91 and -1.96 vs -25.93 for coherence and
non-redundancy, respectively. We also computed the percentage of unique
n-grams in each generated summary for both datasets, as shown in Table 5. The
results support that query-based summaries are less redundant. However,
similar to findings in Oved and Levy (2021), we observe that more coherent
summaries tend to get lower ROUGE scores. Nevertheless, our model outperforms
PASS by a margin on both datasets – by 2.72 and 0.9 ROUGE-L points on Amazon
and Yelp, respectively.

#### Comparison to Baselines.

To understand better how our query-based model compares to other models, we
performed an additional human evaluation experiment. We used the following
criteria: coherence, non-redundancy, and fluency. As previously, we used the
Best-Worst scaling on the Amazon test set. We assigned three AMT workers to
each tuple containing summaries from PASS, AdaQSum (5%) + l1o, LexRank, and
human annotators (Gold).

| Fluency | Coher. | Non-Red.
---|---|---|---
PASS | -21.74 | +33.33 | 0.00
LexRank | -45.95 | -52.38 | -58.97
AdaQSum (5%) + l1o | +26.67 | +25.00 | +26.67
Gold | +46.67 | +27.78 | +55.56
|  |  |
Table 6: Human evaluation results in terms of the Best-Worst scaling on the
Amazon test set.

The results in Table 6 suggest that summaries produced by our model are more
fluent and non-redundant than the ones produced by PASS. In general, PASS
produces more diverse and detailed summaries yet with more semantic mistakes
that make them harder to understand (hence lower fluency scores). However,
summaries by both systems are similarly preferred in terms of coherence. Also,
we note that PASS utilizes a separately trained classifier on human-annotated
summaries (Fabbri et al., 2021) to rank candidate summaries, while our
approach does not.

#### Input Content Fidelity.

As was shown in Falke et al. (2019); Tay et al. (2019), the ROUGE metric can
be insensitive to hallucinations (Maynez et al., 2020). However,
hallucinations can lead to user aversion, and their reduction remains an open
problem in summarization. To assess the input fidelity of generated summaries,
we performed a human evaluation. Specifically, we used summaries produced by
the adapter models (AdaSum (5%) + l1o and AdaQSum (5%) + l1o), FewSum, PASS,
and human-written (Gold). In each task (HIT), we presented both reviews and
all summary sentences. We asked three workers to assess how well the content
in summary sentences is supported by the reviews. The three following options
were available. Full support: all the content is reflected in the reviews;
Partial support: only some content is reflected in the reviews; No support:
content is not reflected in the reviews. The results, normalized by sentences,
are shown in Table 7.

| Full$\uparrow$ | Partial$\uparrow$ | No$\downarrow$
---|---|---|---
FewSum | 47.56 | 24.39 | 28.05
PASS | 60.70 | 31.84 | 7.46
AdaSum (5%) + l1o | 78.97 | 15.48 | 5.56
AdaQSum (5%) + l1o | 72.69 | 20.37 | 6.94
|  |  |
Table 7: Input fidelity on the Amazon test set, normalized by sentences.

First, we observe that FewSum hallucinates the most, potentially because it
was not initialized with a pre-trained language model. Second, PASS improves
input fidelity over FewSum yet substantially underperforms our adapter-based
models. We also notice a slight decrease in input fidelity when the query is
used. This is likely caused by more abstractive summaries generated by
AdaQSum, we discuss it in Sec. 5.3.

##  5 Analysis

###  5.1 Query-based Pre-training

Query-based summarizers should generate summaries reflecting all aspects in
user queries to be useful in practice. We investigated how summarizers learn
this task in the few-shot regime with and without pre-training. We created
test-time queries from gold summaries (indicated by ∗) and input reviews.
Further, we calculated the aspect recall (AR) score by counting aspect
keywords in queries present in generated summaries. The results are shown in
Table 8.

As indicated by low AR scores, without pre-training, the models miss many
aspects in queries. The increase to nearly 100% in AR suggests that pre-
training is crucial for the task. The same trend remains when aspect keywords
from reviews are used in queries.

| R1 | R2 | RL | AR
---|---|---|---|---
Full (100%) + q∗ | 40.52 | 10.96 | 25.06 | 59.84
Full (100%) + l1o + q∗ | 42.65 | 11.53 | 26.82 | 96.39
AdaQSum (5%)∗ | 41.04 | 11.08 | 25.46 | 60.64
AdaQSum (5%) + l1o∗ | 43.84 | 13.41 | 27.31 | 97.19
AdaQSum (5%) | 38.58 | 10.10 | 24.19 | 69.14
AdaQSum (5%) + l1o | 38.53 | 10.52 | 26.06 | 98.78
|  |  |  |
Table 8: Amazon test set ROUGE F1 for query-based summarization. Here, ∗
indicates that queries were created from gold summaries; AR stands for aspect
recall.

###  5.2 Catastrophic Forgetting

ROUGE scores in Table 4 suggest that l1o pre-training is beneficial for the
end task. However, fine-tuning on summaries can lead to the catastrophic
forgetting of the acquired in-domain specifics from reviews. Because adapters
have fewer parameters to optimize, we hypothesized that they might be more
robust to this phenomenon.

To test the hypothesis, we evaluated two models on the pre-training l1o pairs
where a review is used as a summary, before and after fine-tuning on human-
written summaries. For the first model, we optimized only adapters (5%), both
in pre-training and fine-tuning. And in the second case, we optimized the
entire model. We used PPL to measure the model’s confusion about the pre-
training pseudo summaries, as shown in Table 9.

The results demonstrate that the adapter-based model better preserves
information about reviews after they are fine-tuned on summaries, as indicated
by lower PPL scores. Our findings are also supported by Yu et al. (2021).

| PPL$\downarrow$ |
---|---|---
Full (100%) + l1o | 21.51 |
Full (100%) + l1o + ft | 34.87 (+13.36) |
AdaSum (5%) + l1o | 19.69 |
AdaSum (5%) + l1o \+ ft |  28.45 (+8.76) |
|  |
Table 9: Catastrophic forgetting evaluation on the Amazon pre-training task’s
validation set, before and after fine-tuning (FT).

###  5.3 Abstractiveness

Abstracting information in reviews is important for practical applications
(Carenini and Cheung, 2008). To investigate how well the models abstract, we
computed the number of novel n-grams in generated summaries with respect to
input reviews on the Amazon test set. The results in percentages are shown in
Table 10.

First, we observe that FewSum tends to produce the most abstractive summaries,
followed by PASS. Second, AdaQSum has higher abstractivness than AdaSum. We
also observe that abstractiveness is inversely proportional to input
faithfulness in Table 7, in line with previous studies (Durmus et al., 2020;
Dreyer et al., 2021).

| 2-gram | 3-gram | 4-gram
---|---|---|---
FewSum | 78.63 | 95.59 | 98.74
PASS | 70.72 | 86.32 | 93.24
AdaSum (5%) + l1o | 55.47 | 78.24 | 86.78
AdaQSum (5%) + l1o | 56.27 | 79.18 | 88.48
|  |  |
Table 10: The abstractiveness of generated summaries in terms of novel n-grams
on the Amazon test set.

###  5.4 Semantic Mistakes

When a pre-trained model (with and without adapters) is fine-tuned on a
handful of annotated samples, it often results in summaries with subtle
semantic mistakes; see examples in Table 11. For instance, a 5% adapter model
generates a semantically contradicting fragment: ‘This dead on arrival battery
is of good quality and holds a charge well.’.

We hypothesize that it is caused by the lack of in-domain knowledge, which we
propose to learn via l1o (see Sec. 2.4). During a manual investigation, we
observed that l1o pre-training substantially reduces semantic mistakes. This
is also reflected in higher ROUGE scores in Table 4.

|  AdaSum (5%) |  This Thomas the Train costume is cute and functional. The size is perfect for a toddler or 2 year old and the candy pouch is large enough to carry a lot of candy. The costume can be customized with googly eyes, pumpkin patch, spiders, bats, or train tracks to make it more suitable for a child of any age. The fit is comfortable and the fit quality is great. The only complaint I have is the quality of the felt fabric.
---|---|---
AdaSum (5%) + l1o |  This Thomas the Train costume is very cute and the size is perfect for a 2-year-old. The hat is thin and flimsy and the face is not 3D sculpted. The candy pouch is a nice feature and it looks like it will grow with the child. Overall, it’s a cute costume and will be used for Halloween next year.
|  AdaSum (5%) |  This dead on arrival battery is of good quality and holds a charge well. It is easy to install and is a great value for the money. However, it may not hold a charge as advertised due to the plastic case bulging. Overall, this product is highly recommended.
AdaSum (5%) + l1o |  This battery is a great value for the price and works great. It is a good quality battery that can be used to replace a dead battery in an alarm system. The price is great and the quality of the product is good. The shipping was fast and the customer service was excellent.
|  |
Table 11: Adapter-based models (5%) and their generated outputs with and
without l1o pre-training. Semantic mistakes and disfluencies are highlighted
in bold.

##  6 Related Work

Extractive opinion summarization has been an active area of research (Hu and
Liu, 2004; Ganesan et al., 2010; Medhat et al., 2014; Isonuma et al., 2019;
Angelidis et al., 2020). For example, a more recent extractive method of
Angelidis and Lapata (2018) decouples the summarization procedure into
multiple steps with separate models. Other earlier approaches (Gerani et al.,
2014; Di Fabbrizio et al., 2014) relied on text planners and templates, which,
however, restrict the output.

Abstractive opinion summarization is an emerging branch (Chu and Liu, 2019;
Amplayo and Lapata, 2020; Bražinskas et al., 2020b, 2021a). Customer reviews
were used to train unsupervised summarizers in Amplayo and Lapata (2020);
Bražinskas et al. (2020b); Isonuma et al. (2021). The few-shot model FewSum
(Bražinskas et al., 2020a) was also pre-trained on customer reviews before
fine-tuning. In this work instead, we focus on pre-training adapters to avoid
catastrophic forgetting and reduce computational and memory overheads.
OpinionDigest (Suhara et al., 2020) proposes to aggregate opinions in a
pipeline framework. We approach the problem end-to-end and rely on aspect
keywords (e.g., price) instead of opinion phrases (e.g., good location).
Controllability using input fragments (e.g., entities) and meta information
(e.g., coarse-grained aspects) has received recent attention in various NLP
domains (Frermann and Klementiev, 2019; Liu and Chen, 2021; Narayan et al.,
2021; Elsahar et al., 2021). In contrast to a related work, SelfSum (Elsahar
et al., 2021), we use aspect keywords instead of generic tokens and consider a
few-shot setup instead of unsupervised and a different model architecture.
Also, planning was tackled in opinion summarizeration in Amplayo et al.
(2021b). However, their approach is substantially less flexible, as the
summary plan consists of an aspect and sentiment classes only. Query-based
settings have received recent attention in the news domain (Xu and Lapata,
2020, 2021). Compared to a concurrent work on opinion summarization AceSum
(Amplayo et al., 2021a), our approach does not require a trained aspect
induction model, is few-shot instead of self-supervised, and benefits from a
large collection of automatically created fine-grained aspects (a couple of
thousands) instead of human annotated coarse-grained aspects (up to 18).
Concurrently with our work, Poth et al. (2021) support our findings on the
benefits of pre-training adapters for other tasks.

##  7 Conclusions

In this work, we improve few-shot learning for opinion summarization with
adapters pre-training on customer reviews in the end task-specific manner. In
this way, the model learns in-domain specifics, which reduces semantic
mistakes in generated summaries. We show that our approach leads to more than
2.0 and 1.3 ROUGE-L points improvement over the entire model’s fine-tuning on
the Amazon and Yelp datasets, respectively. Further, we propose a simple
method for few-shot query-based summarization. The queries consist of aspect
keywords reflecting potential user interests. We create these queries
automatically and show that pre-training is crucial for the end task,
significantly improving performance. Finally, in human evaluation, we
demonstrate that the query-based model generates more coherent and less
redundant summaries.

## Acknowledgments

We would like to thank Jonathan Pilault, Leonardo Ribeiro, Sandeep Atluri, the
Amazon AI teams for their useful feedback and discussions. Also, we would like
to thank the anonymous reviewers for their insightful comments and
suggestions.

##  8 Ethics Statement

We used only publicly available datasets. For human evaluation, we used a
publicly available service (Amazon Mechanical Turk) to hire voluntary
participants, requesting native speakers of English. The participants were
fairly compensated, above the minimum hourly wage in their self-identified
countries of residence.

##  9 Limitations

In this work, we explicitly focus on multi-document abstractive opinion
summarization. However, our pre-training self-supervised method and fine-
tuning techniques can be applied to a broader set of multi-document
summarization domains (e.g., news) and can be considered in the future work.
Also, while we tested our approach only with BART, we believe that it would
work with other pre-trained encoder-decoder models, like PEGASUS (Zhang et
al., 2020).

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##  10 Appendices

###  10.1 Human Evaluation Setup

To performed the human evaluation experiments described in Sec. 4.2, we hired
workers with 98% approval rate, 1000+ HITS, Location: USA and the maximum
score on a qualification test that we had designed. The test asked if the
workers were native English speakers, and verified that they correctly
understood the instructions of both the best-worst scaling and content support
tasks. We paid the workers an approximate amount of $12 per hour.

###  10.2 Best-Worst Scaling Details

We performed human evaluation based on the Amazon test set using the AMT
platform. We assigned 3 workers to each tuple containing summaries from
different systems. We showed summaries and asked to select the best and worst
one based on the criterion presented below.

  1. 1.

Fluency: the summary sentences should be grammatically correct, easy to read
and understand;

  2. 2.

Coherence: the summary should be well structured and well organized;

  3. 3.

Non-redundancy: there should be no unnecessary repetition in the summary.

Gold |  These transition tights are perfect for children sensitive to the tight sensation other tights have around the foot. The material is soft and durable; they stand up well to both the rough nature of children, and the washing machine. This product does tend to run slightly small, so purchasing one size up is recommended.
---|---
FewSum |  These tights are a great value for the price. The fit is true to size and the quality of the tights is very good. They are well made and will last a long time. They do run a little on the small side, so order a size up.
PASS |  These soft, breathable tights are great for transitioning from tap to ballet. They fit snugly around the body and stay in place when worn with ballet shoes. They are well made and well made, and can last longer than other tights available. The colors are beautiful and will definitely be purchasing again.
AdaSum (5%) + l1o |  These tights are soft and comfortable and fit well. They are durable and will last a long time. They can be worn with sandals or flip-flops. They do run small and should be ordered one size up to avoid squishing toes. The color is beautiful and the material is soft and durable.
AdaQSum (5%) + l1o |  These tights are soft, comfortable, and durable. The color is beautiful and the fit is perfect for tap and ballet. They fit well and are durable enough to wear with flip-flops to class. They are recommended to order one size up if your child is chubby or slim.
Review 1 |  These are the perfect tights for my 5-year old. The tights are very well made and have already lasted several washings (hang dry). The color is beautiful, and my daughter loves that she can wear flip-flops to class like the big girls do.
Review 2 |  my 3 year old fit into these perfectly. I love these tights, they are great for wearing sandals to dance class and then pulling them over her toes to put ballet slippers on. They are nice and soft and the pink color is pretty. Will purchase again.
Review 3 |  These are my daughters preferred ballet tights. They fit well and don’t squish her toes as much as some others. The convertible option is nice as she can wear flip flops to the studio with her tights. I like that they appear to be fairly durable.
Review 4 |  Bought this for my little one to use for her ballet class. She’s almost 4 and this fits perfectly. Transition tights give her the ability to pull up the foot area to around the ankles so that they don’t get dirty when not wearing her shoes, but fit well and stay in place when pulled over her feet and used with ballet shoes on.
Review 5 |  Great soft fabric, runs small a though. U should consider getting one size up to avoid having your daughter, or son if he’s into ballet, have little circulation. Don’t get me wrong, great product and material.
Review 6 |  I purchased these tights for my 4 year old because she has a tap class immediately followed by an acro class. Tights fit well and were easy to transition to bare feet after tap. I can’t comment on how they hold up, as we only had one week of dance so far.
Review 7 |  This is my go to tight when my daughter needs new ones. I won’t buy anything else. They are super comfy and last forever! She is 7 but very slim and these will fit her for a while but the 8-12 is a better fit for longevity purposes but they only had this size left and I needed them right away.
Review 8 |  These are great tights. If your girl is chubby on the waist i think you should order a number up. they are a strong tihgts. My daugther has it for more than 4 months and hasn’t broken them. She only complains some days about the waist, just when she has had a lot of milk or food recently. she’s 3 years old.
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Table 12: Example summaries produced by different systems for an Amazon product. We highlight in bold keywords that were part of the query for AdaQSum (5%) + l1o. Gold |  The results with this scanner are sporadic at best- there are many problems with the software drivers and the stringent system requirements that come along with it. It does not work with the most recent versions of Windows, and the company charges for tech support calls.
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FewSum |  This is a great scanner. It is easy to use and easy to set up. The software that comes with it does not work with Windows Vista. It would be nice if it was a little more user friendly, but for the price it can’t be beat.
PASS |  The VuPoint Digital Scanner is very user friendly and easy to use. It can scan over 1200 35 mm negatives and about a dozen slides. It takes time and money to get the scanner to fit in the holders that come with it. The company also charges $10.00 per phone call for tech support.
AdaSum (5%) + l1o |  The VuPoint Digital Scanner is easy to use and does a great job converting negatives to digital format. However, the software is not compatible with newer versions of Windows. The company ArcSoft charges $10 per phone call for tech support. Overall, this product is not recommended.
AdaQSum (5%) + l1o |  The VuPoint Digital Scanner does a great job of converting photo negatives to digital format. The software is easy to use and easy to install. However, the image bleaches out with too much light. A replacement unit is required. Overall, this product is recommended.
Review 1 |  I recently bought this film and slide scanner to scan my grandfather’s slide collection. It bleaches out the image with too much light. I tried changing the settings to improve the image quality, but had no luck. The company ArcSoft charges $10.00 per phone call for tech support. You are better off making the investment on a nicer quality scanner.
Review 2 |  * * Not Reccommended * * Purchased as a gift in August. Opened a week ago. Spent the last week trying to get Win Xp to recognise the Vu Point scanner. Many drivers and reloads later all I have is a little black box with a red light and a message from windows that says ’USB Device Not Recognised’.
Review 3 |  I used the VuPoint Digital Scanner to scan over 1,200 35 mm negatives and about a dozen slides and found this gadget a most user-friendly and efficient tool. I even managed to upload a few black and white negatives from 1963. I recommend the product highly.
Review 4 |  While the software was good for Windows XP and Vista, I now have Windows 7 and would like to have software for the newer operating system. The company prefers to sell other products rather than update their software. I can’t see recommending this product in today’s market.
Review 5 |  While most equipment will work with more modern versions of Windows than were available when manufactured this is not true with this scanner. Requires Windows XP means it won’t work with earlier OR LATER. Its on its way back for a refund.
Review 6 |  I found the VuPoint scanner not acceptable and I am still waiting for a replacement. My contacts with VuPoint were helpful but the equipment still did not produce acceptable images. My contact with the seller has been sporadic, at best, and a replacement unit has not been delivered.I an NOT anxious to deal with these providers again.
Review 7 |  Product is very easy to use. Does a great job converting my slide and photo negatives to digital format. Touch-up and enhance program gave me just what I needed to clean up and enhance some of the scans, Company was great to work with!!
Review 8 |  Its not worth the time it takes to get the negative to fit in the holders they give you. I’d much rather buy a hp flat bed scanner that lets you see the final photo image and not just an image of the negative. It takes to much time and isn’t worth the money.
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Table 13: Example summaries produced by different systems for an Amazon
product. We highlight in bold keywords that were part of the query for AdaQSum
(5%) + l1o.


$\displaystyle=\big{|}\max\big{\\{}0,1+\max_{i\in[k]}\\{-v_{i}\\}\big{\\}}-\max\big{\\{}0,1+\max_{i\in[k]}\\{-v^{\prime}_{i}\\}\big{\\}}\big{|}$
$\displaystyle\leq|\max_{i\in[k]}\\{-v_{i}\\}-\max_{i\in[k]}\\{-v^{\prime}_{i}\\}|\leq\max_{i\in[k]}|v_{i}-v^{\prime}_{i}|=\|\mathbf{v}-\mathbf{v}^{\prime}\|_{\infty},$

where we have used the elementary inequality

$|\max_{i\in[k]}a_{i}-\max_{i\in[k]}b_{i}|\leq\max_{i\in[k]}|a_{i}-b_{i}|.$

This proves Part (a).

We now prove Part (b). It is clear that

$\frac{\partial\ell(\mathbf{v})}{\partial
v_{i}}=\frac{-\exp(-v_{i})}{1+\sum_{i\in[k]}\exp(-v_{i})}.$

Therefore, the $\ell_{1}$ norm of the gradient can be bounded as follows

$\|\nabla\ell(\mathbf{v})\|_{1}\leq\frac{1}{1+\sum_{i\in[k]}\exp(-v_{i})}\sum_{i\in[k]}\exp(-v_{i})\leq
1.$

This proves Part (b). The proof is completed. ∎