Convex Aggregation for Opinion Summarization

Hayate Iso, Xiaolan Wang, Yoshihiko Suhara, Stefanos Angelidis, Wang-Chiew Tan


Abstract
Recent advances in text autoencoders have significantly improved the quality of the latent space, which enables models to generate grammatical and consistent text from aggregated latent vectors. As a successful application of this property, unsupervised opinion summarization models generate a summary by decoding the aggregated latent vectors of inputs. More specifically, they perform the aggregation via simple average. However, little is known about how the vector aggregation step affects the generation quality. In this study, we revisit the commonly used simple average approach by examining the latent space and generated summaries. We found that text autoencoders tend to generate overly generic summaries from simply averaged latent vectors due to an unexpected L2-norm shrinkage in the aggregated latent vectors, which we refer to as summary vector degeneration. To overcome this issue, we develop a framework Coop, which searches input combinations for the latent vector aggregation using input-output word overlap. Experimental results show that Coop successfully alleviates the summary vector degeneration issue and establishes new state-of-the-art performance on two opinion summarization benchmarks. Code is available at https://github.com/megagonlabs/coop.
Anthology ID:
2021.findings-emnlp.328
Original:
2021.findings-emnlp.328v1
Version 2:
2021.findings-emnlp.328v2
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venue:
Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3885–3903
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.328
DOI:
10.18653/v1/2021.findings-emnlp.328
Bibkey:
Cite (ACL):
Hayate Iso, Xiaolan Wang, Yoshihiko Suhara, Stefanos Angelidis, and Wang-Chiew Tan. 2021. Convex Aggregation for Opinion Summarization. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 3885–3903, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Convex Aggregation for Opinion Summarization (Iso et al., Findings 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.findings-emnlp.328.pdf
Video:
 https://aclanthology.org/2021.findings-emnlp.328.mp4
Code
 megagonlabs/coop