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A Multi-Granularity Opinion Summarization Method

2022-01-16 · ACL ARR January 2022 1 · Anonymous

Existing opinion mining (OM) is limited to applications on commercial reviews, with aspect and sentiment of the opinions in a coarse-grained form. In this paper, we further explore the definition of OM by extending the concepts of aspect and sentiment, and propose an opinion summarization method based on Multi-granularity Clustering and BERT (Jacob et al., 2018), i.e., MCB for emergent online discussion record in keeping with the further definition. A supporting Chinese corpus, ZH45 comprising 45 groups of discussion, and assorted metrics are also proposed. Experiments based on ZH45 and the metrics demonstrate that MCB produces succinct and insightful opinion summaries.

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ClusteringOpinion MiningOpinion Summarization

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Multi-Head Attention 설명 없음
Attention 설명 없음
Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.
Dense Connections Dense Connections, or Fully Connected Connections, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output…
Adam 설명 없음
Attention Dropout Attention Dropout is a type of dropout used in attention-based architectures, where elements are randomly dropped out of the…
Linear Warmup With Linear Decay Linear Warmup With Linear Decay is a learning rate schedule in which we increase the learning rate linearly for $n$ updates and then linearly decay afterwards.
Layer Normalization Unlike batch normalization, Layer Normalization directly estimates the normalization statistics from the summed inputs…

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