Abstract
Recent approaches for unsupervised opinion summarization have predominantly used the review reconstruction training paradigm. An encoder-decoder model is trained to reconstruct single reviews and learns a latent review encoding space. At summarization time, the unweighted average of latent review vectors is decoded into a summary. In this paper, we challenge the convention of simply averaging the latent vector set, and claim that this simplistic approach fails to consider variations in the quality of input reviews or the idiosyncrasies of the decoder. We propose Coop, a convex vector aggregation framework for opinion summarization, that searches for better combinations of input reviews. Coop requires no further supervision and uses a simple word overlap objective to help the model generate summaries that are more consistent with input reviews. Experimental results show that extending opinion summarizers with Coop results in state-of-the-art performance, with ROUGE-1 improvements of 3.7% and 2.9% on the Yelp and Amazon benchmark datasets, respectively.
Abstract (translated)
URL
https://arxiv.org/abs/2104.01371
