Noisy Pairing and Partial Supervision for Stylized Opinion Summarization
This addresses the need for stylized summaries in domains like business analysis, but it is incremental as it builds on existing opinion summarization methods by adding style adaptation.
The paper tackles the problem of generating opinion summaries in a desired professional style from customer reviews, proposing a non-parallel training framework called NAPA that improves automatic and human evaluations on benchmarks like ProSum and FewSum.
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