Corpus ID: 219177126

ExplainIt: Explainable Review Summarization with Opinion Causality Graphs

@article{Carmeli2020ExplainItER,
  title={ExplainIt: Explainable Review Summarization with Opinion Causality Graphs},
  author={Nofar Carmeli and Xiaolan Wang and Yoshihiko Suhara and Stefanos Angelidis and Yuliang Li and Jinfeng Li and Wang Chiew Tan},
  journal={ArXiv},
  year={2020},
  volume={abs/2006.00119}
}
We present ExplainIt, a review summarization system centered around opinion explainability: the simple notion of high-level opinions (e.g. "noisy room") being explainable by lower-level ones (e.g., "loud fridge"). ExplainIt utilizes a combination of supervised and unsupervised components to mine the opinion phrases from reviews and organize them in an Opinion Causality Graph (OCG), a novel semi-structured representation which summarizes causal relations. To construct an OCG, we cluster… Expand
Abstractive Opinion Tagging
TLDR
An abstractive opinion tagging framework, named AOT-Net, is proposed, to generate a ranked list of opinion tags given a large number of reviews, and a large-scale dataset is created, called eComTag, crawled from real-world e-commerce websites to facilitate the study of this task. Expand

References

SHOWING 1-10 OF 42 REFERENCES
Unsupervised Neural Single-Document Summarization of Reviews via Learning Latent Discourse Structure and its Ranking
TLDR
This paper focuses on the end-to-end abstractive summarization of a single product review without supervision, and learns the latent discourse tree without an external parser and generates a concise summary. Expand
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. Expand
Hierarchical Transformers for Multi-Document Summarization
TLDR
A neural summarization model which can effectively process multiple input documents and distill Transformer architecture with the ability to encode documents in a hierarchical manner is developed. Expand
Text Generation from Knowledge Graphs with Graph Transformers
TLDR
This work addresses the problem of generating coherent multi-sentence texts from the output of an information extraction system, and in particular a knowledge graph by introducing a novel graph transforming encoder which can leverage the relational structure of such knowledge graphs without imposing linearization or hierarchical constraints. Expand
Opinosis: A Graph Based Approach to Abstractive Summarization of Highly Redundant Opinions
TLDR
A novel graph-based summarization framework (Opinosis) that generates concise abstractive summaries of highly redundant opinions that have better agreement with human summaries compared to the baseline extractive method. Expand
A Deep Reinforced Model for Abstractive Summarization
TLDR
A neural network model with a novel intra-attention that attends over the input and continuously generated output separately, and a new training method that combines standard supervised word prediction and reinforcement learning (RL) that produces higher quality summaries. Expand
Get To The Point: Summarization with Pointer-Generator Networks
TLDR
A novel architecture that augments the standard sequence-to-sequence attentional model in two orthogonal ways, using a hybrid pointer-generator network that can copy words from the source text via pointing, which aids accurate reproduction of information, while retaining the ability to produce novel words through the generator. Expand
A biterm topic model for short texts
TLDR
The approach can discover more prominent and coherent topics, and significantly outperform baseline methods on several evaluation metrics, and is found that BTM can outperform LDA even on normal texts, showing the potential generality and wider usage of the new topic model. Expand
Opinion Word Expansion and Target Extraction through Double Propagation
TLDR
This article study two important problems, namely, opinion lexicon expansion and opinion target extraction, and proposes a method based on bootstrapping that outperforms these existing methods significantly. Expand
Neural Summarization by Extracting Sentences and Words
TLDR
This work develops a general framework for single-document summarization composed of a hierarchical document encoder and an attention-based extractor that allows for different classes of summarization models which can extract sentences or words. Expand
...
1
2
3
4
5
...