2,638 research outputs found
Self-Supervised and Controlled Multi-Document Opinion Summarization
We address the problem of unsupervised abstractive summarization of
collections of user generated reviews with 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. We address the problem of hallucinations through the use of control
codes, to steer the generation towards more coherent and relevant
summaries.Finally, we extend the Transformer architecture to allow for multiple
reviews as input. Our benchmarks on two datasets against graph-based and recent
neural abstractive unsupervised models show that our proposed method generates
summaries with a superior quality and relevance.This is confirmed in our human
evaluation which focuses explicitly on the faithfulness of generated summaries
We also provide an ablation study, which shows the importance of the control
setup in controlling hallucinations and achieve high sentiment and topic
alignment of the summaries with the input reviews.Comment: 18 pages including 5 pages appendi
Classification of Smartphone Application Reviews Using Small Corpus Based on Bidirectional LSTM Transformer
This paper provides the classification of the review texts on a smartphone application posted on social media. We propose a high performance binary classification method (positive/negative) of review texts, which uses the bidirectional long short-term memory (biLSTM) self-attentional Transformer and is based on the distributed representations created by unsupervised learning of a manually labelled small review corpus, dictionary, and an unlabeled large review corpus. The proposed method obtained higher accuracy as compared to the existing methods, such as StarSpace or the Bidirectional Encoder Representations from Transformer (BERT)
Leveraging contextual embeddings and self-attention neural networks with bi-attention for sentiment analysis
People express their opinions and views in different and often ambiguous ways, hence the meaning of their words is often not explicitly stated and frequently depends on the context. Therefore, it is difficult for machines to process and understand the information conveyed in human languages. This work addresses the problem of sentiment analysis (SA). We propose a simple yet comprehensive method which uses contextual embeddings and a self-attention mechanism to detect and classify sentiment. We perform experiments on reviews from different domains, as well as on languages from three different language families, including morphologically rich Polish and German. We show that our approach is on a par with state-of-the-art models or even outperforms them in several cases. Our work also demonstrates the superiority of models leveraging contextual embeddings. In sum, in this paper we make a step towards building a universal, multilingual sentiment classifier.Peer ReviewedPostprint (published version
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