Identifying Helpful Sentences in Product Reviews

@article{Gamzu2021IdentifyingHS,
  title={Identifying Helpful Sentences in Product Reviews},
  author={Iftah Gamzu and Hila Gonen and Gilad Kutiel and Ran Levy and Eugene Agichtein},
  journal={ArXiv},
  year={2021},
  volume={abs/2104.09792}
}
In recent years online shopping has gained momentum and became an important venue for customers wishing to save time and simplify their shopping process. A key advantage of shopping online is the ability to read what other customers are saying about products of interest. In this work, we aim to maintain this advantage in situations where extreme brevity is needed, for example, when shopping by voice. We suggest a novel task of extracting a single representative helpful sentence from a set of… 
Classifier Construction Under Budget Constraints
TLDR
A heuristic algorithm is devised, whose effectiveness is demonstrated in the experimental study over real-world data, consisting of a public dataset and datasets provided by a large e-commerce company that include costs and scores derived by business analysts.
Review-Based Tip Generation for Music Songs
TLDR
A dataset named MTips is created for the tip generation task and a framework named G EN TMS for automatically generating tips from song reviews is proposed, which achieves top-10 precision at 85.56%, outperforming the baselinemodelsbyatleast3.34%.
Generating Tips from Song Reviews: A New Dataset and Framework
TLDR
A dataset named MTips is created for the tip generation task and a framework named G EN TMS for automatically generating tips from song reviews is proposed, which achieves top-10 precision at 85.56%, outperforming the baselinemodelsbyatleast3.34%.
Helping Voice Shoppers Make Purchase Decisions
TLDR
This paper reports on a within-subject user study, in which three template-based methods that use information from customer reviews, product attributes and search relevance signals to generate helpful supporting information are employed.
Pairwise Review-Based Explanations for Voice Product Search
TLDR
A crowd-sourced evaluation of explanations based on queries from a widely used e-commerce platform shows that the proposed pairwise explanations provide statistically significant improvements compared to the POINTWISE and BASELINE methods for two goals: Effectiveness, i.e. helping users to make good decisions, and Transparency, i,e. explaining how the system works.
Which kind of rumors may undermine society: perspectives from court orders
Freedom of speech is one of the principles in the constitution of most countries. However, in the 2020 United States presidential election, Donald Trump's Twitter account is suspended due to the risk
Decision-Focused Summarization
TLDR
This work proposes a novel problem, decision-focused summarization, where the goal is to summarize relevant information for a decision, and leverages a predictive model that makes the decision based on the full text to provide valuable insights on how a decision can be inferred from text.

References

SHOWING 1-10 OF 40 REFERENCES
Corpus Analysis and Annotation for Helpful Sentences in Product Reviews
TLDR
A new annotation scheme is proposed to identify helpful sentences from each product review in the dataset, and a high level of inter-annotator agreement is obtained, indicating that the annotated corpus is suitable to support subsequent research.
Mining and summarizing customer reviews
TLDR
This research aims to mine and to summarize all the customer reviews of a product, and proposes several novel techniques to perform these tasks.
Few-Shot Learning for Opinion Summarization
TLDR
This work shows that even a handful of summaries is sufficient to bootstrap generation of the summary text with all expected properties, such as writing style, informativeness, fluency, and sentiment preservation.
Semantic Analysis and Helpfulness Prediction of Text for Online Product Reviews
TLDR
This paper isolates review helpfulness prediction from its outer layer tasks, employ two interpretable semantic features, and use human scoring of helpfulness as ground truth, and results show that the two semantic features can accurately predict helpfulness scores and greatly improve the performance compared with using features previously used.
Few-Shot Learning for Abstractive Multi-Document Opinion Summarization
TLDR
This work shows that even a handful of summaries is sufficient to bootstrap generation of the summary text with all expected properties, such as writing style, informativeness, fluency, and sentiment preservation.
Automatically Assessing Review Helpfulness
TLDR
This paper considers the task of automatically assessing review helpfulness, and finds that the most useful features include the length of the review, its unigrams, and its product rating.
Multi-Document Summarization of Evaluative Text
TLDR
A framework for summarizing a corpus of evaluative documents about a single entity by a natural language summary is presented and it is indicated that forevaluative text abstraction tends to be more effective than extraction, particularly when the corpus is controversial.
Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised
TLDR
An opinion summarization dataset is introduced that includes a training set of product reviews from six diverse domains and human-annotated development and test sets with gold standard aspect annotations, salience labels, and opinion summaries.
Unsupervised Opinion Summarization as Copycat-Review Generation
TLDR
A generative model for a review collection is defined which capitalizes on the intuition that when generating a new review given a set of other reviews of a product, the authors should be able to control the “amount of novelty” going into the new review or, equivalently, vary the extent to which it deviates from the input.
RevRank: A Fully Unsupervised Algorithm for Selecting the Most Helpful Book Reviews
TLDR
A novel method for content anal- ysis, which is especially suitable for product reviews, and shows that REVRANK clearly outperforms a baseline imitating the user vote model used by Amazon.
...
...