Noisy Pairing and Partial Supervision for Stylized Opinion Summarization

TL;DR

This paper introduces a new task of stylized opinion summarization, aiming to generate summaries in specific writing styles from customer reviews, and proposes a non-parallel training framework called NAPA to achieve this.

Contribution

It presents the NAPA framework for training stylized summarization models without parallel data and creates a new benchmark dataset ProSum for evaluation.

Findings

NAPA improves automatic evaluation scores

NAPA enhances human judgment of summary quality

The model successfully generates professionally styled summaries

Abstract

Opinion summarization research has primarily focused on generating summaries reflecting important opinions from customer reviews without paying much attention to the writing style. In this paper, we propose the stylized opinion summarization task, which aims to generate a summary of customer reviews in the desired (e.g., professional) writing style. To tackle the difficulty in collecting customer and professional review pairs, we develop a non-parallel training framework, Noisy Pairing and Partial Supervision (NAPA), which trains a stylized opinion summarization system from non-parallel customer and professional review sets. We create a benchmark ProSum by collecting customer and professional reviews from Yelp and Michelin. Experimental results on ProSum and FewSum demonstrate that our non-parallel training framework consistently improves both automatic and human evaluations, successfully building a stylized opinion summarization model that can generate professionally-written summaries from customer reviews. The code is available at https://github.com/megagonlabs/napa