Extractive Opinion Summarization in Quantized Transformer Spaces

Extractive Opinion Summarization in Quantized Transformer Spaces

AbstractWe 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 s...

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Bibliographic Details
Main Authors: Stefanos Angelidis, Reinald Kim Amplayo, Yoshihiko Suhara, Xiaolan Wang, Mirella Lapata
Format: Article
Language:English
Published: The MIT Press 2021-01-01
Series:Transactions of the Association for Computational Linguistics
Online Access:https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00366/98621/Extractive-Opinion-Summarization-in-Quantized

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https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00366/98621/Extractive-Opinion-Summarization-in-Quantized

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