Related papers: Extractive Opinion Summarization in Quantized Transformer Spaces

Extractive Opinion Summarization in Quantized Transformer Spaces

URL: http://arxiv.org/abs/2012.04443v1
Date: Tue, 8 Dec 2020 14:23:46 GMT
Title: Extractive Opinion Summarization in Quantized Transformer Spaces
Authors: Stefanos Angelidis, Reinald Kim Amplayo, Yoshihiko Suhara, Xiaolan Wang, Mirella Lapata
Abstract summary: We present the Quantized Transformer (QT), an unsupervised system for extractive opinion summarization. QT is inspired by Vector-Quantized Variational Autoencoders, which we repurpose for popularity-driven summarization.
Score: 52.95867345952894
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: We present the Quantized Transformer (QT), an unsupervised system for extractive opinion summarization. QT is inspired by Vector-Quantized Variational Autoencoders, which we repurpose for popularity-driven summarization. It uses a clustering interpretation of the quantized space and a novel extraction algorithm to discover popular opinions among hundreds of reviews, a significant step towards opinion summarization of practical scope. In addition, QT enables controllable summarization without further training, by utilizing properties of the quantized space to extract aspect-specific summaries. We also make publicly available SPACE, a large-scale evaluation benchmark for opinion summarizers, comprising general and aspect-specific summaries for 50 hotels. Experiments demonstrate the promise of our approach, which is validated by human studies where judges showed clear preference for our method over competitive baselines.

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