Opinion summarization is the task of automatically creating summaries that reflect subjective
information expressed in multiple documents, such as product reviews. While the majority of …

Abstract We present the Quantized Transformer (QT), an unsupervised system for extractive
opinion summarization. QT is inspired by Vector-Quantized Variational Autoencoders, which …

Rhetorical structure theory (RST) is a significant theory about discourse organization. With
an increasing number of research interests focus on RST, many novel parsing approaches …

We present OpinionDigest, an abstractive opinion summarization framework, which does not
rely on gold-standard summaries for training. The framework uses an Aspect-based …

Opinion summarization is the automatic creation of text reflecting subjective information
expressed in multiple documents, such as user reviews of a product. The task is practically …

BERT, a neural network-based language model pre-trained on large corpora, is a
breakthrough in natural language processing, significantly outperforming previous state-of …

Opinion summarization is the task of automatically generating summaries that encapsulate
information from multiple user reviews. We present Semantic Autoencoder (SemAE) to …

In this paper, we propose FFCI, a framework for fine-grained summarization evaluation that
comprises four elements: faithfulness (degree of factual consistency with the source), focus …

As labeling schemas evolve over time, small differences can render datasets following older
schemas unusable. This prevents researchers from building on top of previous annotation …

Recently, document-level neural machine translation (NMT) has become a hot topic in the
community of machine translation. Despite its success, most of existing studies ignored the …