Unsupervised Aspect-Based Multi-Document Abstractive Summarization

Coavoux, Maximin; Elsahar, Hady; Gallé, Matthias

doi:10.18653/v1/d19-5405

Cited by 28 publications

(29 citation statements)

References 10 publications

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“…Product review summarization is a form of multi-document summarization in which a set of product reviews for a single product serves as the document cluster to be summarized. A common approach for product review summarization, which centers the summary around a set of extracted aspects and their respective sentiment, is termed aspect-based summarization (Hu and Liu, 2004;Kansal and Toshniwal, 2014;Wu et al, 2016;Angelidis and Lapata, 2018;Coavoux et al, 2019).…”

Section: Related Workmentioning

confidence: 99%

PASS: Perturb-and-Select Summarizer for Product Reviews

Oved¹,

Levy²

2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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The product reviews summarization task aims to automatically produce a short summary for a set of reviews of a given product. Such summaries are expected to aggregate a range of different opinions in a concise, coherent and informative manner. This challenging task gives rise to two shortcomings in existing work. First, summarizers tend to favor generic content that appears in reviews for many different products, resulting in template-like, less informative summaries. Second, as reviewers often disagree on the pros and cons of a given product, summarizers sometimes yield inconsistent, self-contradicting summaries. We propose the PASS system (Perturb-and-Select Summarizer) that employs a large pre-trained Transformer-based model (T5 in our case), which follows a few-shot fine-tuning scheme.A key component of the PASS system relies on applying systematic perturbations to the model's input during inference, which allows it to generate multiple different summaries per product. We develop a method for ranking these summaries according to desired criteria, coherence in our case, enabling our system to almost entirely avoid the problem of selfcontradiction. We compare our system against strong baselines on publicly available datasets, and show that it produces summaries which are more informative, diverse and coherent. 1 * Completed during an internship at Amazon.

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Section: Related Workmentioning

confidence: 99%

PASS: Perturb-and-Select Summarizer for Product Reviews

Oved¹,

Levy²

2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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“…These baselines were selected because the previous evaluation task concludes that the term clustering is the more promising and effective setting for topic modeling in our proposal. Thus, it allows us to evaluate the performance of the different approaches of our model and to compare them with notable summarizers as TextRank [41] (a similar decision is adopted in References [46,47]).…”

Section: Resultsmentioning

confidence: 99%

Towards Context-Aware Opinion Summarization for Monitoring Social Impact of News

et al. 2020

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Opinion mining and summarization of the increasing user-generated content on different digital platforms (e.g., news platforms) are playing significant roles in the success of government programs and initiatives in digital governance, from extracting and analyzing citizen’s sentiments for decision-making. Opinion mining provides the sentiment from contents, whereas summarization aims to condense the most relevant information. However, most of the reported opinion summarization methods are conceived to obtain generic summaries, and the context that originates the opinions (e.g., the news) has not usually been considered. In this paper, we present a context-aware opinion summarization model for monitoring the generated opinions from news. In this approach, the topic modeling and the news content are combined to determine the “importance” of opinionated sentences. The effectiveness of different developed settings of our model was evaluated through several experiments carried out over Spanish news and opinions collected from a real news platform. The obtained results show that our model can generate opinion summaries focused on essential aspects of the news, as well as cover the main topics in the opinionated texts well. The integration of term clustering, word embeddings, and the similarity-based sentence-to-news scoring turned out the more promising and effective setting of our model.

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“…However, averaging representations of statements that are sometimes contradictory tends to confuse the decoder, and might lead it to ignore the input signal. To deal with this limitation, Coavoux et al (2019) add a clustering step to group similar reviews and to generate one sentence per such found cluster. Bražinskas et al (2020) proposed to solve the problem of unsupervised opinion summarization with an auto-encoder with latent variables.…”

Section: Related Workmentioning

confidence: 99%

Self-Supervised and Controlled Multi-Document Opinion Summarization

Elsahar¹,

Coavoux²,

Rozen³

et al. 2021

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

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We address the problem of unsupervised abstractive summarization of collections of user generated reviews through self-supervision and control. We propose a self-supervised setup that considers an individual document as a target summary for a set of similar documents. This setting makes training simpler than previous approaches by relying only on standard log-likelihood loss and mainstream models. We address the problem of hallucinations through the use of control codes, to steer the generation towards more coherent and relevant summaries. Our benchmarks on two English datasets against graph-based and recent neural abstractive unsupervised models show that our proposed method generates summaries with a superior quality and relevance, as well as a high sentiment and topic alignment with the input reviews. This is confirmed in our human evaluation which focuses explicitly on the faithfulness of generated summaries. We also provide an ablation study showing the importance of the control setup in controlling hallucinations.

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Unsupervised Aspect-Based Multi-Document Abstractive Summarization

Cited by 28 publications

References 10 publications

PASS: Perturb-and-Select Summarizer for Product Reviews

PASS: Perturb-and-Select Summarizer for Product Reviews

Towards Context-Aware Opinion Summarization for Monitoring Social Impact of News

Self-Supervised and Controlled Multi-Document Opinion Summarization

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