OPINESUM: Entailment-based self-training for abstractive opinion summarization
Year: 2023

Abstract:
A typical product or place often has hundreds of reviews, and summarization of these texts is a challenging problem. Recent progress on abstractive summarization in domains such as news has been driven by supervised systems trained on hundreds of thousands of news articles paired with human-written summaries. However for opinion texts, such large scale datasets are rarely available. Unsupervised methods, self-training, and few-shot learning approaches bridge that gap. In this work, we present a novel self-training approach, OPINESUM for abstractive opinion summarization. The self-training summaries in this approach are built automatically using a novel application of textual entailment and capture the consensus of opinions across the various reviews for an item. This method can be used to obtain silver-standard summaries on a large scale and train both unsupervised and few-shot abstractive summarization systems. OPINESUM outperforms strong peer systems in both settings.

heading: 1 Introduction
text: Abstractive summarization is particularly promising for fluently comparing opinions from a set of reviews about a place or product. However, while language models trained on huge numbers of source-summary pairs have driven summarization performance in domains such as news, it is harder to find such pairs on the web for opinions, and immensely difficult to present tens or hundreds of reviews to human annotators and train them to write informative summaries. This paper presents a new self-training approach that automatically leverages common opinions across reviews, for example as in Table 1, to create powerful abstractive models.
So far, many abstractive summarization methods for opinions are based on auto-encoders (Chu and Liu, 2019; Bražinskas et al., 2020b; Isonuma et al., 2021), and do not use any supervision from
∗ Both authors contributed equally to the work.
gold human summaries. A few recent approaches propose self-training of encoder-decoder models on a task of predicting missing reviews from topically related ones (Amplayo and Lapata, 2020; Bražinskas et al., 2020a; Amplayo et al., 2021; Elsahar et al., 2021; Brazinskas et al., 2022). While this method is greatly useful for pretraining, inherently the objective predicts full review texts (which standardly contain a lot of non-summary worthy content as well), and has only weak signals for how to aggregate content across multiple reviews.
We present an improved self-training method based on automatically generated silver-standard summaries. These silver summaries use textual entailment to locate the consensus or most agreed upon user opinions in the source reviews, and present them as self-training signals. For the example in Table 1, multiple mentions that “the location is right across balboa park”, intuitively make this statement a worthy candidate for a summary of the hotel. In an improvement over prior self-training methods, our silver-summaries are high quality and combine information across multiple reviews. We show how to generate these silver summaries on a large scale to train encoder-decoder transformer models. We evaluate our models in unsupervised as well as few-shot learning, and show gains on both
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content quality and fluency of output summaries.

heading: 2 Related work
text: Opinion summarization is a widely studied problem, where the role of sentiment, and product aspects (such as ‘lens’ and ‘focus’ for a camera) are well documented. We focus on abstractive methods for general-purpose summaries (not aspect-based), and we overview the closest approaches here.
Unsupervised neural networks. As in other areas of text generation, modern opinion summarization methods are also predominantly neural networks. Since large scale training data is largely absent in this domain, many prior methods are unsupervised. Common techniques include auto-encoders (Chu and Liu, 2019) and associated generative approaches such as VAEs (Bražinskas et al., 2020b; Isonuma et al., 2021). The underlying goal in these approaches is also to steer systems towards common information in the source reviews.
Our work also presents an unsupervised method, but based on encoder-decoder models.
Self-training methods. Some very recent solutions take advantage of recent large pretrained encoder-decoder models via self-training (Amplayo and Lapata, 2020; Bražinskas et al., 2020a; Amplayo et al., 2021; Elsahar et al., 2021; Brazinskas et al., 2022). These approaches create large number of pairs of source review sets, paired with a pseudo or silver summary as an approximate target. In all these methods, one of the reviews from the source set is taken as the pseudo summary, and other random reviews or topically related reviews to the target is taken as the set of source reviews. This dataset is then used for further training of encoder-decoder transformer models to suit the review domain. These self-trained models are usually better than unsupervised generative ones.
While allowing a favorable paradigm shift, and better performance, there are a few limitations of this type of self-training. As pointed out by Bražinskas et al. (2020a), reviews are considerably diverse from one another. So an objective that generates a review from other reviews will need to also predict content not present on the source side, a major difference from actual summaries of reviews. Such pseudo-summaries also contain a lot of first person language which is less desirable.
In this work, we present a novel self-training method. We also create silver-summaries on a large
scale. However, our summaries actually contain propositions from multiple input reviews and in particular those which are reflective of the consensus among the review authors. These summaries are more powerful and faithful signals, and move the training task away from review generation.
Few-shot learning. With increased use of encoderdecoder models, methods have also been proposed to efficiently augment the training with a small number of human-generated summaries (50 to 100). Oved and Levy (2021) train transformer models on a small number of examples and use a ranking approach for inference. In Bražinskas et al. (2020a), a plug-in network is added to predict desired summary properties and thereby augment training signals. Brazinskas et al. (2022) introduce a few additional parameters in the form of adaptors and only these are finetuned instead of the full network, leading to efficient training on few examples.
We also demonstrate our self-trained model in few-shot settings.
Consensus as a goal for summarization. When there are multiple input texts, intuitively the common information across them is one important signal for summary-worthy content. Multi-document news summarization has exploited frequency from early times (Nenkova et al., 2006; Radev et al., 2004) to most recent ones (Ernst et al., 2022a). Consensus is also used as a goal for summarizing scientific publications around health topics (Shah et al., 2021), and identifying agreement and discrepancies in Wikipedia document clusters (Schuster et al., 2022). Instructions to annotators in multiple annotation efforts for opinion summarization explicitly ask annotators to capture what is common and popular (Bražinskas et al., 2020a; Angelidis et al., 2021). Most recently, agreement among opinions has been proposed for evaluating opinion summaries (Bhaskar et al., 2022).
Our self-training approach explicitly captures consensus statements. We acknowledge that majority opinions are only one indicator of useful content, and that others (eg. aspects) will complement it.

heading: 3 Textual entailment to identify consensus among review users
text: We propose a novel approach to create silver source-summary pairs for abstractive opinion summarization. A central idea here is the use of textual entailment to find statements reflecting user consen-
sus. We first present our definition of the idea and describe the steps involved in silver data creation.

heading: 3.1 Defining review consensus
text: We define consensus as the number of reviews that support a claim. For example, 60 (out of 100) reviews might claim that the seafood dishes are great at a restaurant. Likewise 40 reviews might say that the staff are friendly. We aim to identify such popular claims (high consensus) automatically.
But note that the same claim may be expressed in different ways or granularity, and so its frequency in reviews cannot be easily computed. Eg. ‘This hotel is in the heart of Times Square’ and ‘Hotel’s location is slap bang in the middle of Times Square.’ are the same claim, and ‘The fish is tasty’ and ‘The salmon is delicious’, both support the claim that
‘The seafood is great.’. Our idea is to accomodate this variability using natural language entailment.
At a high level, our approach identifies potential claims in the form of propositions from a large collection of texts, uses textual entailment to find out how often the collection supports the proposition, and computes a score for the support.
Now we explain how we obtain these statements and their scores automatically.

heading: 3.2 Extracting propositions
text: For texts, even when they are sentence-level units, it is hard to reason about them precisely. Many review sentences in addition tend to be rather long. For example, “The room was very nice and clean, quiet location, staff were helpful, easy access to the centre of town by metro, bakeries and a supermarket nearby.” contain a bunch of different claims. It is difficult to find support for such complex sentences since the same information is unlikely to be present in other users’ reviews.
Instead, we split review sentences into propositions and use these as our key units. We define a proposition as a ‘single claim or fact’ about the item and extract these as snippets from the original review texts. In fact, recent work on supervised news summarization uses the extraction and clustering of proposition units to find frequent subtopics, and then fuses the information in the biggest clusters into a summary (Ernst et al., 2022b).
Social media text is noisy with missing punctuation, and even syntactically, the long sentences are often multiple sentence-type units/claims concatenated into one, in contrast to embedded clauses which are typical of complex sentences in news
etc. So existing approaches for proposition detection such as OpenIE (Stanovsky et al., 2018) which are trained on news and center on predicates, often miss detecting parts of long sentences on our domain. So, we use simple rules to split review sentences into propositions. Of course, there is room to improve proposition creation in future.
We split sentences at conjunctions, period, and comma subject to a minimum clause length of four. Our algorithm processes sentences from left to right to find a delimiter. If the proposed span will create a clause less than the minimum length, we do not split and attach the span to the proposition on the left. Note that these propositions are a linear segmentation of the input sentence, and their concatenation yields the original sentence. Intuitively, this process performs syntactic simplification, without changing the total content that is expressed.
The resulting propositions for different sentences in our data is shown in Table 2. Note that there are some propositions which end up ungrammatical, and our length constraints do not always separate out all the aspects (as in the third example). But overall this simple method works well for review sentences where syntactic embedding is less complex than in genres such as news, and we can scale to large collections efficiently.
We extract propositions from all the reviews for an item. Suppose there are N reviews for an item which result in M propositions, then M N .

heading: 3.3 Scoring consensus
text: Our aim is to find the number of supporting reviews for each of the M propositions. We compute this number using natural language entailment. Specifically, consider review Ri and proposition mj belonging to the same item. Let us represent a textual entailment relation as P → H , where P is a premise and H is a hypothesis. In our case, if Ri → mj , then we consider that Ri supports mj . The final score for proposition mj , S(mj) = ∑ 1≤i≤N E(Ri,mj) where E(Ri,mj) = 1 if Ri → mj else 0. We obtain E(Ri,mj) using the predictions of an entailment classifier which treats Ri as the premise and mj as the hypothesis. If the top label from the classifier is ‘entailment’, then E(Ri,mj) = 1 and 0 if other labels had the highest probability.
In this work, we use a cross attention model, BERT-large (Devlin et al., 2019) to obtain these predictions. The input to the model concatenates
the premise and hypothesis with a separator symbol, and the CLS token’s embedding is sent through a linear layer to predict three classes: entailment, contradiction and neutral. We trained this model on the MNLI corpus (Williams et al., 2018) reaching a development accuracy of 84%. Note that the training data for the entailment model does not contain any examples from the review domain. But we found that predictions are reasonable and even better when a higher threshold is applied on the probability of the entailment label.
Note that this score computation for all propositions requires an entailment prediction between all pairs of (Ri,mj). Even though the computation is done only within each item, there are still a quadratic number of pairs per item.
So we implement the full computation of silver summaries in a Apache Beam1 pipeline which allows to create parallel data-processing pipelines. Our typical pipelines do inference billions of times by the entailment models. It is also for this reason that we used BERT large since it was the largest cross-attention model that gave us the best tradeoff between accuracy and inference speed. For our silver data, the pipeline involved 1.3B entailment predictions taking 30hrs.
In Table 3, we show some of the entailment predictions from our models.

heading: 3.4 Silver summaries
text: We order the propositions in decreasing order of their scores S(mi), and take the top n as the silver summary sentences. We trim the silver summary up to a certain summary length expressed in tokens. Additionally, we employ a MMR (Goldstein and Carbonell, 1998) style redundancy removal technique to keep diverse content in the summary. We implement this control using a simple method of content word overlap.2 Suppose S is
1https://beam.apache.org/ 2We also explored entailment based diversity measures, but we found that simple content word overlap kept the maximum diversity in aspects commented on within the summaries. Entailment is a strict notion focusing on the truth of the hy-
the set of propositions selected in the summary so far. The next proposition chosen is the highest scoring proposition pk where overlap(pk, si) < 2, ∀i, 1 ≤ i ≤ |S|. overlap is computed as the number of content words in common. We used the stopword list within NLTK (Bird et al., 2009).
The top propositions for two hotel items from our dataset is shown in Table 4.
This final set of summary propositions, S, chosen for a given summary length, are then concatenated in the chosen order to create the silver summary. When the propositions are not full sentences, we polish them for capitalization and punctuation to match natural texts. Some other disfluencies remain and several are related to the noisy nature of the text. We note that in most cases, the list of top propositions is a very reasonable summary, and in this first work, we have not carried out further processing for coherence.

heading: 3.5 Source texts
text: The silver summaries from the previous step are composed of extracted propositions from the source reviews. A system trained to produce such sequences from the full set of input reviews will predominantly copy from the input texts. So we make changes to the set of source reviews to make the data suitable for abstractive summarization.
Let N be the total set of input reviews. For each proposition pi in the summary, we remove the entire review Rj , where pi came from, i.e. pi is a span in Rj . This deletion discourages the verbatim copying of text spans from the source. The final input reviews on the source side is a reduced set N ′, |N ′| < |N |. Note that sentences (propositions) in the silver standard are supported by many other reviews, albeit in different surface forms, so the signals to produce the silver summary are still present
pothesis. The claims: (i) ‘The hotel has a warm pool’ and (ii) ‘The hotel had a big pool’ do not entail each other, and that works during consensus detection, but these claims on the same topic add redundancy in a summary. Word overlap was a more flexible way to focus on diversity. In the absence of data/models for redundancy detection, we used the best settings chosen empirically on dev. data.
in N ′. An illustration of input review selections is shown in Figure 1. This way of creating sourcesummary pairs resembles one of the powerful pretraining objectives for abstractive summarization known as Gap Sentences Generation, introduced by the Pegasus (Zhang et al., 2020) model.
In practice, the number of source reviews that can be processed as input to most standard sequence to sequence models is fewer than the hundreds present in N ′. So we sample a smaller set N ′′, size k, of reviews to fit the sequence length of the encoder. We could further aid the training by adapting N ′′ to be most useful for generating the target sentences. We sample l reviews from the entailment set of each summary proposition, where l is proportional to the size of the entailment set. For example, if ‘seafood is great’ is a summary proposition with 40% entailment support, then 40% of the summarization input are reviews on the topic of great seafood, although the review containing the verbatim proposition is filtered out.3 In this way, the source always contains entailing reviews for every summary proposition. So our silver data explicitly guards against hallucinations.
In the next sections, we describe how we use this data to train abstractive summarization systems.
3We tried other sampling methods, but they performed similarly during development.

heading: 4 Datasets
text: We use two types of data in our experiments: unlabelled and eval. The first is an unlabelled review corpus (no gold or human summaries are available for the items). This dataset is used to create silverstandard summaries for self-training. The second type is an evaluation dataset containing a much smaller set of items (not seen during training) and here for each item, the set of source reviews is paired with one or more human summaries.
We use publicly available anonymized corpora of reviews and summaries as per their terms.
SPACE-unlabelled. The SPACE corpus collected by Angelidis et al. (2021), comprises of reviews for hotels from the TripAdvisor website. There are a total of 1.1 million reviews for 11,000 hotels. These reviews are not paired with human summaries. We use this dataset for silver-summary creation.
We use two sources of gold-standard abstractive summaries for evaluating our systems.
SPACE-eval. The SPACE corpus (Angelidis et al., 2021) also contains gold summaries for a small set of 50 hotels (25 dev and 25 test). For each of these hotels, 100 input reviews are paired with 3 human-written abstractive summaries. The humansummaries were created via a two-step process where annotators first selected key sentences from the input reviews, and then wrote a general summary based on the sentence collection.
This evaluation dataset ideally suits our task since the input contains 100 reviews on which one could ask for common opinions and claims.
AMAZON-eval. This dataset (Bražinskas et al., 2020a) contains summaries for reviews of Amazon products. There are 60 products (13 dev, 20 test and 28 train). The training items are only used for fewshot learning experiments. For each product, there are only 8 (randomly chosen) input reviews on the source side, and with 3 human-written abstractive summaries. These inputs comprising 8 random reviews offer little scope for common themes across them. Previous few-shot learning studies use this corpus, so we include it for comparison while acknowledging that the inputs are less than ideal.

heading: 5 Experiments
text: In this section, we explain how we trained our abstractive summarization models.

heading: 5.1 Models
text: We build our abstractive systems using pretrained encoder-decoder models based on T5’s (Raffel et al., 2020) framework. These models encode the input reviews as a sequence and autoregressively generate the output summary words as a sequence.
In multi-document summarization, especially opinions, source reviews are easily in the hundreds. Standard self-attention layers found in current transformer models have a polynomial scale relationship to input length, making it impossible to encode and attend to several reviews at once. Many summarization systems avoid this issue by including a content selection component as a first step of a pipeline. Recent work has shown that sparse transformers can overcome this issue, simplifying models and often outperforming pipeline based alternatives. For this reason, we have built models on top of LongT5 (Guo et al., 2022), which implements sparse attention and allows tokens to attend locally and globally via transient global nodes.
We employ LongT5 models (size: Large with 770M parameters) with a limit of 8,192 sentence pieces. We use the public pretrained checkpoint. 4 Larger sizes XL (3B parameters) did not improve performance. We also compared how many reviews, size k, should be present on the input side. Typically more reviews, 160 (filling the model’s sequence length), performed the best.
During development, LongT5 models always outperformed their T5 counterparts.

heading: 5.2 Silver Data
text: We create our silver data using the SPACEunlabelled corpus (Section 4) and follow the procedure outlined in Section 3.
We retained 4,729 items with a minimum of 50 reviews (since very few reviews may not have a lot in common to extract out). Our beam pipelines computed around 1.3B entailment predictions on the review-proposition pairs from these items. The resulting silver data has the same number of items, but now each item is paired with a silver summary.

heading: 5.3 Self-training
text: We explore the usefulness of our self-training in two setups: unsupervised and few-shot learning. For the unsupervised case, we train our models on the silver-data only. For few-shot learning, we use
4https://github.com/google-research/longt5
a small number of annotated input-summary pairs (<100) for finetuning our self-supervised systems.
Unsupervised training. Given the silver-data, we trained LongT5-Large models on the sequence-tosequence task of generating the highest consensus opinions given a concatenated sequence of the input reviews. These models do not use any goldannotated examples for training. We select the best checkpoint based the ROUGE scores (mean of R2 and RL) on the validation set. We compare these systems with prior unsupervised work.
Few-shot Learning. was implemented by finetuning our self-trained models (i.e. trained by unsupervised method above) on a few human annotated source-review and summary pairs. To facilitate this setup, we divide the development examples in SPACE-eval (25 total) into a training set with 15 items and a validation set with 10 items. In our preliminary experiments, we did not find much performance difference in how we chose these sets. The test set remains unchanged. The AMAZONeval data is already divided into 3 sets (Bražinskas et al., 2020a). The best checkpoint was again selected based on ROUGE scores on the validation set. These models trained better with a reduced learning rate, 1/5th of the standard 1e− 4.
In addition to prior few-shot systems, we will also compare these models with transformer baselines which do not use self-training with silver summaries. Rather these baselines are warm started from the public pretrained checkpoints and then similarly finetuned on the (few-shot) train splits.

heading: 6 Results
text: We present our findings on the SPACE-eval and AMAZON-eval testsets ( Section 4). We compute the automatic ROUGE (Lin, 2004) metric, and also confirm the improvements with human raters. For comparison, we included recent systems where the best system outputs were available publicly.

heading: 6.1 Unsupervised models
text: We compare OPINESUM with previous abstractive systems: Meansum (Chu and Liu, 2019), ACESUM (Amplayo et al., 2021) (current best for SPACE), and Copycat (Bražinskas et al., 2020b) (competitive on AMAZON).
Often extractive systems are strong baselines, so we also include: Centroid: a review closest to the mean review vector computed using BERT (Devlin et al., 2019); Lexrank (Erkan and Radev,
2004), Quantized Transformer (Angelidis et al., 2021), Random (review), Lead: first sentences of reviews, and Clustroid: review with highest ROUGE-L scores with other reviews.
To separate out model contributions from those of silver data, we also include a vanilla longT5 system (no silver data). LongT5 was pretrained with a summarization objective (Zhang et al., 2020) and hence already suited to the problem.
These results are in Tables 5 and 6.5 We see that OPINESUM obtains very good performance, achieving the best results on SPACE-eval. Note that these improvements are reached with only a new self-training method and without any additional learning or domain-specific changes which are present in most of peer systems. The improvement is further evidenced from the fact that a vanilla longT5 model by itself is only a weak baseline.
On AMAZON-eval, we are close to copycat but do not outperform it. As we outlined already in Section 4, our self-training aims to capture consensus among input reviews and on this data, the input comprises 8 random reviews with little in common. Hence the self-trained models are less successful at predicting the summary content chosen by those human annotators (especially in an unsupervised manner). Still they are close to one of the best systems and outperform other peers.
5We noticed that ROUGE results from previous papers are not always consistently reproducible, since they either choose to compare peer summaries with a union of multiple references or take the maximum ROUGE from individual reference comparisons. So we computed or obtained output summaries from previous authors and calculated ROUGE scores in a consistent manner (max from 3 comparisons) and report these results. Note hence that these results may differ from that recorded in previous papers, and we only report numbers where we could obtain summaries from authors of previous papers. But our numbers can be systematically compared.

heading: 6.2 Few-shot learning models
text: These results are in Tables 7 and 8. There are no prior few-shot results on SPACE-eval. Nevertheless, T5 models trained without silver-data but with few-shot learning are a strong ablation to compare with OPINESUM. We list these under vanilla fewshot models. On AMAZON-eval, we compare with Fewsum (Bražinskas et al., 2020a) and Adasum (Brazinskas et al., 2022) (See Sec. 2 for details).
Here, the baseline T5 models are already rather strong, especially longT5. Our simple self-training still leads to further improvements on SPACE-eval.
On AMAZON-eval, the LongT5 systems are better than Fewsum but not as good as the current best Adasum which is tailored for efficient few-shot training. As already alluded to, it is also likely our methods have less to leverage across only 8 input reviews. Nevertheless, note that our contribution demonstrates a self-training method alone which can complement other training strategies.
We show an example output of our system compared with gold standards and prior system in the Appendix (Table 10). There is a noteworthy difference between our unsupervised and fewshot systems. The unsupervised system produces shorter summaries, is less adapted to the domain, and at times, contains disfluencies due to being trained on smoothed propositions. Fewshot learning improves along these dimensions making the summary much closer to the gold standards.

heading: 6.3 Human evaluation
text: Our ROUGE results on SPACE-eval show that given sufficient number of input reviews, our selftraining delivers improved summarization performance. We confirm these improvements via a human evaluation, and also examine other dimensions
such as summary coherence. We conduct this evaluation on the 25 test examples in SPACE-eval using crowd annotators. They were presented with the three gold summaries for each example and four system summaries. They were asked to pick the best summary (no ties) for two criteria: (i) content: where the content of the system summary matches best with the gold summaries and (ii) coherence: the most well-written system summary. Each example was rated by 3 annotators. More details are in the Appendix.
We also conducted two rounds of annotations. In the first Eval A, annotators compared summaries from four unsupervised abstractive systems. None of these systems were trained with gold summaries. In the second Eval B, we seek to understand the improvements from few-shot learning where even with few examples, the system is given the opportunity to learn the style of a domain. Table 9 indicates how often each system was picked as best by the raters (out of 75 ratings per question).
The OPINESUM outputs are definitely noticed as higher content quality compared to baselines and on par with the best systems. There a greater edge to these systems on the coherence criteria.

heading: 6.4 Faithfulness of summaries
text: For abstractive systems, faithfulness is also an important dimension of quality. Following standard practice (Maynez et al., 2020; Dušek and Kasner, 2020; Honovich et al., 2022), we use an entailment classifier to ascertain whether the content of OPINESUM’s summary sentences originate from the source reviews.
For a score, we compute the percentage of faithful summary sentences from OPINESUM, across all inputs (i.e. macro-average). A summary sentence Sj is faithful if at least one input review Ri entails Sj . We use the entailment classifier from Honovich et al. (2022)6 which comprises a T5-11B model (Raffel et al., 2020) trained on the ANLI dataset (Nie et al., 2020). We set the threshold for entailment label at 0.7 for greater precision on the out-of-domain review examples.
We do this evaluation on the SPACE-eval dataset where we found the entailment predictions to be more reliable. Here we find that 98.6% of all sentences from an unsupervised OPINESUM model are faithful to the source. As described in Section 3.5, our models are explicitly trained to produce sentences which are entailed by the source reviews. This aspect has likely led to the high performance.
We do not report these scores for other systems as they produce longer sentences, and hence have an outright disadvantage when entailment is computed for their sentence-level units. For OPINESUM models, the sentences are shorter and correspond to propositions and it is easier to check accurately if they are entailed by the source.
While not comparing systems, we find that OPINESUM summary faithfulness is desirably high.
6https://github.com/google-research/true

heading: 6.5 Errors analysis
text: To further understand these performance numbers, we present sample outputs in Tables 10 and 11.
Table 11 presents unsupervised summaries for two inputs from the SPACE-eval test set. For both these inputs, all the three human annotators in our evaluation (see Section 6.3) rated OPINESUM summaries as the best, both in terms of content quality and coherence. The text of these summaries provides some insights into properties which might correlate with the quality judgements.
A distinguishing property of unsupervised OPINESUM outputs is that the text is composed of small sentences corresponding to propositions. Each of these relates to a single aspect. On the other hand, other unsupervised systems produce longer sentences. We observe that in many such long sentences there is considerable incoherence among the parts e.g. from ACESUM: there is a lot to do if you are looking for a good place to stay, but the location is very close to the beach and the beach is just a block away from the sand beach. This difference could lead to the perceived higher content quality and coherence of OPINESUM texts. During few-shot learning, OPINESUM summaries adjust to longer length sentences as in Table 10.
We also note that redundancy is a quality issue across the summaries including OPINESUM, and this is also an avenue to improve for future work.

heading: 7 Conclusion
text: We have presented a simple self-training approach which leads to gains on both unsupervised and fewshot abstractive opinion summarization.
Our work is one of the first to demonstrate how an intuitive idea of review consensus can be incorporated during self-training. It opens up a number of challenges and new problems for future work. In particular, while our silver data contains the provenance for each top proposition—meaning the set of reviews which support each the proposition— this information is only minimally used at the moment. Future work could explore how the entailment weights (scores) of each proposition and their links to entailing reviews could be used for further improvements and faithful generation.
We also hope that such self-training models could serve as good checkpoints for other tasks in the opinion domain such as review helpfulness or product popularity prediction.
Limitations
In this first effort, OPINESUM was demonstrated for the English language. Since the wealth of review information on many websites span several languages, extending our work to other languages is a key area for future work. There are two language specific components—the proposition identification rules, and the textual entailment model. For the latter, there are multilingual resources such as mT5 models (Xue et al., 2021) and multilingual entailment datasets (Conneau et al., 2018) which are good starting points. The proposition rules are much more language specific. Very recent work has introduced a corpus and learned model for proposition identification (Chen et al., 2022), and future research in languages other than English could strength this component.
A second noteworthy point is the scalability of the silver data creation. As described in Section 3.3, we perform a quadratic number of entailment queries per item. In this work, this was of the order of a few billion. We used an Apache beam pipeline to scale our computation using a lot of parallel computation on CPUs. Readers must be aware of this computation when applying such an approach for their work. However, note that the processing only needs to be performed once for training data creation. Future work on more efficient transformer models such as Khattab and Zaharia (2020), will help vastly improve these types of computations.
Ethics Statement
This paper focuses on abstractive summarization where text is generated using an encoder-decoder model. Hence typical issues associated with text generation must be kept in mind while assessing the outputs from such models. As language models improve in faithfulness and accuracy of generated texts, we expect our systems to benefit similarly.

heading: Acknowledgements
text: We would like to thank Ryan McDonald, Filip Radlinski, and Andrey Petrov for their suggestions and feedback on our work.

heading: A Example system output
text: Table 10 shows the outputs from our best unsupervised and few-shot learning systems compared with gold standards and best prior approach.
For error analysis, in Table 11, we present various unsupervised summaries for two inputs from the SPACE-eval test set. For both these inputs, all the three human annotators in our evaluation (see Section 6.3) rated OPINESUM summaries as the best, both for content quality and coherence.

heading: B Human evaluation
text: We employed three crowd annotators for our manual evaluation. The instructions provided to them are in Figure 2.
ACL 2023 Responsible NLP Checklist

heading: A For every submission:
text: 3 A1. Did you describe the limitations of your work?
8
3 A2. Did you discuss any potential risks of your work? 9
3 A3. Do the abstract and introduction summarize the paper’s main claims? 1
7 A4. Have you used AI writing assistants when working on this paper? Left blank.
B 3 Did you use or create scientific artifacts? We use publicly available datasets for evaluation. Section 4
3 B1. Did you cite the creators of artifacts you used? 4
3 B2. Did you discuss the license or terms for use and / or distribution of any artifacts? 4

heading: 3 B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided
text: that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? 4

heading: 3 B4. Did you discuss the steps taken to check whether the data that was collected / used contains any
text: information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? 4

heading: 3 B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and
text: linguistic phenomena, demographic groups represented, etc.? 4
3 B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. 4
C 3 Did you run computational experiments? 5

heading: 3 C1. Did you report the number of parameters in the models used, the total computational budget
text: (e.g., GPU hours), and computing infrastructure used? 5 and 3.3
The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance.

heading: 3 C2. Did you discuss the experimental setup, including hyperparameter search and best-found
text: hyperparameter values? 5

heading: 3 C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary
text: statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? 5.3

heading: 3 C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did
text: you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Yes, Section 5 and 6, especially see footnote in Section 6.1
D 3 Did you use human annotators (e.g., crowdworkers) or research with human participants? 6.3

heading: 3 D1. Did you report the full text of instructions given to participants, including e.g., screenshots,
text: disclaimers of any risks to participants or annotators, etc.? Section 6.3 and Appendix B

heading: 3 D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students)
text: and paid participants, and discuss if such payment is adequate given the participants’ demographic (e.g., country of residence)? We recruited 3 crowd annotators on a proprietary platform discussed in Section 6.3. However, due to privacy concerns, we did not include the estimated hourly wage paid to participants or the total amount spent on participant compensation. We feel that individuals’ hourly wage or compensation is personal information and we cannot disclose this under privacy law. However, this work was carried out by paid contractors, and we can confirm that they received their standard contracted wage, which is above the living wage in their country of employment.
7 D3. Did you discuss whether and how consent was obtained from people whose data you’re using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Not discussed in the paper. But annotators were told the task was evaluation of system summaries and instructions were provided to them before data collection.
7 D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? We use standard evaluation questions for summary outputs on public data, so we did not need an approval process.
7 D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? We used 3 English-speaking annotators but do not collect any demographic data.

heading: Future Work
text: It opens up a number of challenges and new problems for future work. In particular, while our silver data contains the provenance for each top proposition—meaning the set of reviews which support each the proposition— this information is only minimally used at the moment. Future work could explore how the entailment weights (scores) of each proposition and their links to entailing reviews could be used for further improvements and faithful generation. We also hope that such self-training models could serve as good checkpoints for other tasks in the opinion domain such as review helpfulness or product popularity prediction. Limitations
In this first effort, OPINESUM was demonstrated for the English language. Since the wealth of review information on many websites span several languages, extending our work to other languages is a key area for future work. There are two language specific components—the proposition identification rules, and the textual entailment model. For the latter, there are multilingual resources such as mT5 models (Xue et al. , 2021) and multilingual entailment datasets (Conneau et al. , 2018) which are good starting points. The proposition rules are much more language specific. Very recent work has introduced a corpus and learned model for proposition identification (Chen et al. , 2022), and future research in languages other than English could strength this component. A second noteworthy point is the scalability of the silver data creation. As described in Section 3. 3, we perform a quadratic number of entailment queries per item. In this work, this was of the order of a few billion. We used an Apache beam pipeline to scale our computation using a lot of parallel computation on CPUs. Readers must be aware of this computation when applying such an approach for their work. However, note that the processing only needs to be performed once for training data creation. Future work on more efficient transformer models such as Khattab and Zaharia (2020), will help vastly improve these types of computations